Examining Differential Item Functioning: IRT-Based Detection in the Framework of Confirmatory Factor Analysis

ERIC Educational Resources Information Center

Dimitrov, Dimiter M.

2017-01-01

This article offers an approach to examining differential item functioning (DIF) under its item response theory (IRT) treatment in the framework of confirmatory factor analysis (CFA). The approach is based on integrating IRT- and CFA-based testing of DIF and using bias-corrected bootstrap confidence intervals with a syntax code in Mplus.

A Review of Functional Analysis Methods Conducted in Public School Classroom Settings

ERIC Educational Resources Information Center

Lloyd, Blair P.; Weaver, Emily S.; Staubitz, Johanna L.

2016-01-01

The use of functional behavior assessments (FBAs) to address problem behavior in classroom settings has increased as a result of education legislation and long-standing evidence supporting function-based interventions. Although functional analysis remains the standard for identifying behavior--environment functional relations, this component is…

Trade-Off Analysis between Concerns Based on Aspect-Oriented Requirements Engineering

NASA Astrophysics Data System (ADS)

Laurito, Abelyn Methanie R.; Takada, Shingo

The identification of functional and non-functional concerns is an important activity during requirements analysis. However, there may be conflicts between the identified concerns, and they must be discovered and resolved through trade-off analysis. Aspect-Oriented Requirements Engineering (AORE) has trade-off analysis as one of its goals, but most AORE approaches do not actually offer support for trade-off analysis; they focus on describing concerns and generating their composition. This paper proposes an approach for trade-off analysis based on AORE using use cases and the Requirements Conflict Matrix (RCM) to represent compositions. RCM shows the positive or negative effect of non-functional concerns over use cases and other non-functional concerns. Our approach is implemented within a tool called E-UCEd (Extended Use Case Editor). We also show the results of evaluating our tool.

Default-Mode Network Functional Connectivity in Aphasia: Therapy-Induced Neuroplasticity

ERIC Educational Resources Information Center

Marcotte, Karine; Perlbarg, Vincent; Marrelec, Guillaume; Benali, Habib; Ansaldo, Ana Ines

2013-01-01

Previous research on participants with aphasia has mainly been based on standard functional neuroimaging analysis. Recent studies have shown that functional connectivity analysis can detect compensatory activity, not revealed by standard analysis. Little is known, however, about the default-mode network in aphasia. In the current study, we studied…

Alternations of functional connectivity in amblyopia patients: a resting-state fMRI study

NASA Astrophysics Data System (ADS)

Wang, Jieqiong; Hu, Ling; Li, Wenjing; Xian, Junfang; Ai, Likun; He, Huiguang

2014-03-01

Amblyopia is a common yet hard-to-cure disease in children and results in poor or blurred vision. Some efforts such as voxel-based analysis, cortical thickness analysis have been tried to reveal the pathogenesis of amblyopia. However, few studies focused on alterations of the functional connectivity (FC) in amblyopia. In this study, we analyzed the abnormalities of amblyopia patients by both the seed-based FC with the left/right primary visual cortex and the network constructed throughout the whole brain. Experiments showed the following results: (1)As for the seed-based FC analysis, FC between superior occipital gyrus and the primary visual cortex was found to significantly decrease in both sides. The abnormalities were also found in lingual gyrus. The results may reflect functional deficits both in dorsal stream and ventral stream. (2)Two increased functional connectivities and 64 decreased functional connectivities were found in the whole brain network analysis. The decreased functional connectivities most concentrate in the temporal cortex. The results suggest that amblyopia may be caused by the deficits in the visual information transmission.

Teacher-Conducted Trial-Based Functional Analyses as the Basis for Intervention

ERIC Educational Resources Information Center

Bloom, Sarah E.; Lambert, Joseph M.; Dayton, Elizabeth; Samaha, Andrew L.

2013-01-01

Previous studies have focused on whether a trial-based functional analysis (FA) yields the same outcomes as more traditional FAs, and whether interventions based on trial-based FAs can reduce socially maintained problem behavior. We included a full range of behavior functions and taught 3 teachers to conduct a trial-based FA with 3 boys with…

Effect of an Automated Training Presentation on Pre-Service Behavior Analysts' Implementation of Trial-Based Functional Analysis

ERIC Educational Resources Information Center

Lambert, Joseph M.; Lloyd, Blair P.; Staubitz, Johanna L.; Weaver, Emily S.; Jennings, Chelsea M.

2014-01-01

The trial-based functional analysis (FA) is a useful alternative to the traditional FA in contexts in which it is challenging to establish environmental control for extended periods of time. Previous researchers have demonstrated that others can be trained to conduct trial-based FAs with high procedural fidelity by providing a didactic…

Text Mining Improves Prediction of Protein Functional Sites

PubMed Central

Cohn, Judith D.; Ravikumar, Komandur E.

2012-01-01

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. PMID:22393388

Teleconsultation in school settings: linking classroom teachers and behavior analysts through web-based technology.

PubMed

Frieder, Jessica E; Peterson, Stephanie M; Woodward, Judy; Crane, Jaelee; Garner, Marlane

2009-01-01

This paper describes a technically driven, collaborative approach to assessing the function of problem behavior using web-based technology. A case example is provided to illustrate the process used in this pilot project. A school team conducted a functional analysis with a child who demonstrated challenging behaviors in a preschool setting. Behavior analysts at a university setting provided the school team with initial workshop trainings, on-site visits, e-mail and phone communication, as well as live web-based feedback on functional analysis sessions. The school personnel implemented the functional analysis with high fidelity and scored the data reliably. Outcomes of the project suggest that there is great potential for collaboration via the use of web-based technologies for ongoing assessment and development of effective interventions. However, an empirical evaluation of this model should be conducted before wide-scale adoption is recommended.

Classifying Different Emotional States by Means of EEG-Based Functional Connectivity Patterns

PubMed Central

Lee, You-Yun; Hsieh, Shulan

2014-01-01

This study aimed to classify different emotional states by means of EEG-based functional connectivity patterns. Forty young participants viewed film clips that evoked the following emotional states: neutral, positive, or negative. Three connectivity indices, including correlation, coherence, and phase synchronization, were used to estimate brain functional connectivity in EEG signals. Following each film clip, participants were asked to report on their subjective affect. The results indicated that the EEG-based functional connectivity change was significantly different among emotional states. Furthermore, the connectivity pattern was detected by pattern classification analysis using Quadratic Discriminant Analysis. The results indicated that the classification rate was better than chance. We conclude that estimating EEG-based functional connectivity provides a useful tool for studying the relationship between brain activity and emotional states. PMID:24743695

Quantile Functions, Convergence in Quantile, and Extreme Value Distribution Theory.

DTIC Science & Technology

1980-11-01

Gnanadesikan (1968). Quantile functions are advocated by Parzen (1979) as providing an approach to probability-based data analysis. Quantile functions are... Gnanadesikan , R. (1968). Probability Plotting Methods for the Analysis of Data, Biomtrika, 55, 1-17.

Short-term forecasting of meteorological time series using Nonparametric Functional Data Analysis (NPFDA)

NASA Astrophysics Data System (ADS)

Curceac, S.; Ternynck, C.; Ouarda, T.

2015-12-01

Over the past decades, a substantial amount of research has been conducted to model and forecast climatic variables. In this study, Nonparametric Functional Data Analysis (NPFDA) methods are applied to forecast air temperature and wind speed time series in Abu Dhabi, UAE. The dataset consists of hourly measurements recorded for a period of 29 years, 1982-2010. The novelty of the Functional Data Analysis approach is in expressing the data as curves. In the present work, the focus is on daily forecasting and the functional observations (curves) express the daily measurements of the above mentioned variables. We apply a non-linear regression model with a functional non-parametric kernel estimator. The computation of the estimator is performed using an asymmetrical quadratic kernel function for local weighting based on the bandwidth obtained by a cross validation procedure. The proximities between functional objects are calculated by families of semi-metrics based on derivatives and Functional Principal Component Analysis (FPCA). Additionally, functional conditional mode and functional conditional median estimators are applied and the advantages of combining their results are analysed. A different approach employs a SARIMA model selected according to the minimum Akaike (AIC) and Bayessian (BIC) Information Criteria and based on the residuals of the model. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE), relative RMSE, BIAS and relative BIAS. The results indicate that the NPFDA models provide more accurate forecasts than the SARIMA models. Key words: Nonparametric functional data analysis, SARIMA, time series forecast, air temperature, wind speed

Geometric Analysis of Wing Sections

DOT National Transportation Integrated Search

1995-04-01

This paper describes a new geometric analysis procedure for wing sections. This procedure is based on the normal mode analysis for continuous functions. A set of special shape functions is introduced to represent the geometry of the wing section. The...

HSI top-down requirements analysis for ship manpower reduction

NASA Astrophysics Data System (ADS)

Malone, Thomas B.; Bost, J. R.

2000-11-01

U.S. Navy ship acquisition programs such as DD 21 and CVNX are increasingly relying on top down requirements analysis (TDRA) to define and assess design approaches for workload and manpower reduction, and for ensuring required levels of human performance, reliability, safety, and quality of life at sea. The human systems integration (HSI) approach to TDRA begins with a function analysis which identifies the functions derived from the requirements in the Operational Requirements Document (ORD). The function analysis serves as the function baseline for the ship, and also supports the definition of RDT&E and Total Ownership Cost requirements. A mission analysis is then conducted to identify mission scenarios, again based on requirements in the ORD, and the Design Reference Mission (DRM). This is followed by a mission/function analysis which establishes the function requirements to successfully perform the ship's missions. Function requirements of major importance for HSI are information, performance, decision, and support requirements associated with each function. An allocation of functions defines the roles of humans and automation in performing the functions associated with a mission. Alternate design concepts, based on function allocation strategies, are then described, and task networks associated with the concepts are developed. Task network simulations are conducted to assess workloads and human performance capabilities associated with alternate concepts. An assessment of the affordability and risk associated with alternate concepts is performed, and manning estimates are developed for feasible design concepts.

Characterizing bonding patterns in diradicals and triradicals by density-based wave function analysis: A uniform approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Orms, Natalie; Rehn, Dirk; Dreuw, Andreas

Density-based wave function analysis enables unambiguous comparisons of electronic structure computed by different methods and removes ambiguity of orbital choices. Here, we use this tool to investigate the performance of different spin-flip methods for several prototypical diradicals and triradicals. In contrast to previous calibration studies that focused on energy gaps between high and low spin-states, we focus on the properties of the underlying wave functions, such as the number of effectively unpaired electrons. Comparison of different density functional and wave function theory results provides insight into the performance of the different methods when applied to strongly correlated systems such asmore » polyradicals. We also show that canonical molecular orbitals for species like large copper-containing diradicals fail to correctly represent the underlying electronic structure due to highly non-Koopmans character, while density-based analysis of the same wave function delivers a clear picture of bonding pattern.« less

Characterizing bonding patterns in diradicals and triradicals by density-based wave function analysis: A uniform approach

DOE PAGES

Orms, Natalie; Rehn, Dirk; Dreuw, Andreas; ...

2017-12-21

Density-based wave function analysis enables unambiguous comparisons of electronic structure computed by different methods and removes ambiguity of orbital choices. Here, we use this tool to investigate the performance of different spin-flip methods for several prototypical diradicals and triradicals. In contrast to previous calibration studies that focused on energy gaps between high and low spin-states, we focus on the properties of the underlying wave functions, such as the number of effectively unpaired electrons. Comparison of different density functional and wave function theory results provides insight into the performance of the different methods when applied to strongly correlated systems such asmore » polyradicals. We also show that canonical molecular orbitals for species like large copper-containing diradicals fail to correctly represent the underlying electronic structure due to highly non-Koopmans character, while density-based analysis of the same wave function delivers a clear picture of bonding pattern.« less

Functional Analysis and Reduction of Inappropriate Spitting

ERIC Educational Resources Information Center

Carter, Stacy L.; Wheeler, John J.

2007-01-01

Functional analysis was used to determine the possible function of inappropriate spitting behavior of an adult woman who had been diagnosed with profound mental retardation. Results of an initial descriptive assessment indicated a possible attention function and led to an attention-based intervention, which was deemed ineffective at reducing the…

Classroom Application of a Trial-Based Functional Analysis

ERIC Educational Resources Information Center

Bloom, Sarah E.; Iwata, Brian A.; Fritz, Jennifer N.; Roscoe, Eileen M.; Carreau, Abbey B.

2011-01-01

We evaluated a trial-based approach to conducting functional analyses in classroom settings. Ten students referred for problem behavior were exposed to a series of assessment trials, which were interspersed among classroom activities throughout the day. Results of these trial-based functional analyses were compared to those of more traditional…

Comparative analysis of taxonomic, functional, and metabolic patterns of microbiomes from 14 full-scale biogas reactors by metagenomic sequencing and radioisotopic analysis.

PubMed

Luo, Gang; Fotidis, Ioannis A; Angelidaki, Irini

2016-01-01

Biogas production is a very complex process due to the high complexity in diversity and interactions of the microorganisms mediating it, and only limited and diffuse knowledge exists about the variation of taxonomic and functional patterns of microbiomes across different biogas reactors, and their relationships with the metabolic patterns. The present study used metagenomic sequencing and radioisotopic analysis to assess the taxonomic, functional, and metabolic patterns of microbiomes from 14 full-scale biogas reactors operated under various conditions treating either sludge or manure. The results from metagenomic analysis showed that the dominant methanogenic pathway revealed by radioisotopic analysis was not always correlated with the taxonomic and functional compositions. It was found by radioisotopic experiments that the aceticlastic methanogenic pathway was dominant, while metagenomics analysis showed higher relative abundance of hydrogenotrophic methanogens. Principal coordinates analysis showed the sludge-based samples were clearly distinct from the manure-based samples for both taxonomic and functional patterns, and canonical correspondence analysis showed that the both temperature and free ammonia were crucial environmental variables shaping the taxonomic and functional patterns. The study further the overall patterns of functional genes were strongly correlated with overall patterns of taxonomic composition across different biogas reactors. The discrepancy between the metabolic patterns determined by metagenomic analysis and metabolic pathways determined by radioisotopic analysis was found. Besides, a clear correlation between taxonomic and functional patterns was demonstrated for biogas reactors, and also the environmental factors that shaping both taxonomic and functional genes patterns were identified.

Collision analysis of one kind of chaos-based hash function

NASA Astrophysics Data System (ADS)

Xiao, Di; Peng, Wenbing; Liao, Xiaofeng; Xiang, Tao

2010-02-01

In the last decade, various chaos-based hash functions have been proposed. Nevertheless, the corresponding analyses of them lag far behind. In this Letter, we firstly take a chaos-based hash function proposed very recently in Amin, Faragallah and Abd El-Latif (2009) [11] as a sample to analyze its computational collision problem, and then generalize the construction method of one kind of chaos-based hash function and summarize some attentions to avoid the collision problem. It is beneficial to the hash function design based on chaos in the future.

Progressing from Identification and Functional Analysis of Precursor Behavior to Treatment of Self-Injurious Behavior

ERIC Educational Resources Information Center

Dracobly, Joseph D.; Smith, Richard G.

2012-01-01

This multiple-study experiment evaluated the utility of assessing and treating severe self-injurious behavior (SIB) based on the outcomes of a functional analysis of precursor behavior. In Study 1, a precursor to SIB was identified using descriptive assessment and conditional probability analyses. In Study 2, a functional analysis of precursor…

Functional Module Search in Protein Networks based on Semantic Similarity Improves the Analysis of Proteomics Data*

PubMed Central

Boyanova, Desislava; Nilla, Santosh; Klau, Gunnar W.; Dandekar, Thomas; Müller, Tobias; Dittrich, Marcus

2014-01-01

The continuously evolving field of proteomics produces increasing amounts of data while improving the quality of protein identifications. Albeit quantitative measurements are becoming more popular, many proteomic studies are still based on non-quantitative methods for protein identification. These studies result in potentially large sets of identified proteins, where the biological interpretation of proteins can be challenging. Systems biology develops innovative network-based methods, which allow an integrated analysis of these data. Here we present a novel approach, which combines prior knowledge of protein-protein interactions (PPI) with proteomics data using functional similarity measurements of interacting proteins. This integrated network analysis exactly identifies network modules with a maximal consistent functional similarity reflecting biological processes of the investigated cells. We validated our approach on small (H9N2 virus-infected gastric cells) and large (blood constituents) proteomic data sets. Using this novel algorithm, we identified characteristic functional modules in virus-infected cells, comprising key signaling proteins (e.g. the stress-related kinase RAF1) and demonstrate that this method allows a module-based functional characterization of cell types. Analysis of a large proteome data set of blood constituents resulted in clear separation of blood cells according to their developmental origin. A detailed investigation of the T-cell proteome further illustrates how the algorithm partitions large networks into functional subnetworks each representing specific cellular functions. These results demonstrate that the integrated network approach not only allows a detailed analysis of proteome networks but also yields a functional decomposition of complex proteomic data sets and thereby provides deeper insights into the underlying cellular processes of the investigated system. PMID:24807868

Studying hemispheric lateralization during a Stroop task through near-infrared spectroscopy-based connectivity

NASA Astrophysics Data System (ADS)

Zhang, Lei; Sun, Jinyan; Sun, Bailei; Luo, Qingming; Gong, Hui

2014-05-01

Near-infrared spectroscopy (NIRS) is a developing and promising functional brain imaging technology. Developing data analysis methods to effectively extract meaningful information from collected data is the major bottleneck in popularizing this technology. In this study, we measured hemodynamic activity of the prefrontal cortex (PFC) during a color-word matching Stroop task using NIRS. Hemispheric lateralization was examined by employing traditional activation and novel NIRS-based connectivity analyses simultaneously. Wavelet transform coherence was used to assess intrahemispheric functional connectivity. Spearman correlation analysis was used to examine the relationship between behavioral performance and activation/functional connectivity, respectively. In agreement with activation analysis, functional connectivity analysis revealed leftward lateralization for the Stroop effect and correlation with behavioral performance. However, functional connectivity was more sensitive than activation for identifying hemispheric lateralization. Granger causality was used to evaluate the effective connectivity between hemispheres. The results showed increased information flow from the left to the right hemispheres for the incongruent versus the neutral task, indicating a leading role of the left PFC. This study demonstrates that the NIRS-based connectivity can reveal the functional architecture of the brain more comprehensively than traditional activation, helping to better utilize the advantages of NIRS.

Functional Analysis in Public Schools: A Summary of 90 Functional Analyses

ERIC Educational Resources Information Center

Mueller, Michael M.; Nkosi, Ajamu; Hine, Jeffrey F.

2011-01-01

Several review and epidemiological studies have been conducted over recent years to inform behavior analysts of functional analysis outcomes. None to date have closely examined demographic and clinical data for functional analyses conducted exclusively in public school settings. The current paper presents a data-based summary of 90 functional…

Training Teachers to Conduct Trial-Based Functional Analyses

ERIC Educational Resources Information Center

Kunnavatana, S. Shanun; Bloom, Sarah E.; Samaha, Andrew L.; Dayton, Elizabeth

2013-01-01

The trial-based functional analysis (FA) is a promising approach to identification of behavioral function and is especially suited for use in educational settings. Not all studies on trial-based FA have included teachers as therapists, and those studies that have, included minimal information on teacher training. The purpose of this study was to…

Parameter Transient Behavior Analysis on Fault Tolerant Control System

NASA Technical Reports Server (NTRS)

Belcastro, Christine (Technical Monitor); Shin, Jong-Yeob

2003-01-01

In a fault tolerant control (FTC) system, a parameter varying FTC law is reconfigured based on fault parameters estimated by fault detection and isolation (FDI) modules. FDI modules require some time to detect fault occurrences in aero-vehicle dynamics. This paper illustrates analysis of a FTC system based on estimated fault parameter transient behavior which may include false fault detections during a short time interval. Using Lyapunov function analysis, the upper bound of an induced-L2 norm of the FTC system performance is calculated as a function of a fault detection time and the exponential decay rate of the Lyapunov function.

Functional Behavioral Assessment: A School Based Model.

ERIC Educational Resources Information Center

Asmus, Jennifer M.; Vollmer, Timothy R.; Borrero, John C.

2002-01-01

This article begins by discussing requirements for functional behavioral assessment under the Individuals with Disabilities Education Act and then describes a comprehensive model for the application of behavior analysis in the schools. The model includes descriptive assessment, functional analysis, and intervention and involves the participation…

Evaluation of Road Performance Based on International Roughness Index and Falling Weight Deflectometer

NASA Astrophysics Data System (ADS)

Hasanuddin; Setyawan, A.; Yulianto, B.

2018-03-01

Assessment to the performance of road pavement is deemed necessary to improve the management quality of road maintenance and rehabilitation. This research to evaluate the road base on functional and structural and recommendations handling done. Assessing the pavement performance is conducted with functional and structural evaluation. Functional evaluation of pavement is based on the value of IRI (International Roughness Index) which among others is derived from reading NAASRA for analysis and recommended road handling. Meanwhile, structural evaluation of pavement is done by analyzing deflection value based on FWD (Falling Weight Deflectometer) data resulting in SN (Structural Number) value. The analysis will result in SN eff (Structural Number Effective) and SN f (Structural Number Future) value obtained from comparing SN eff to SN f value that leads to SCI (Structural Condition Index) value. SCI value implies the possible recommendation for handling pavement. The study done to Simpang Tuan-Batas Kota Jambi road segment was based on functional analysis. The study indicated that the road segment split into 12 segments in which segment 1, 3, 5, 7, 9, and 11 were of regular maintenance, segment 2, 4, 8, 10, 12 belonged to periodic maintenance, and segment 6 was of rehabilitation. The structural analysis resulted in 8 segments consisting of segment 1 and 2 recommended for regular maintenance, segment 3, 4, 5, and 7 for functional overlay, and 6 and 8 were of structural overlay.

Functional analysis and treatment of problem behavior in early education classrooms.

PubMed

Greer, Brian D; Neidert, Pamela L; Dozier, Claudia L; Payne, Steven W; Zonneveld, Kimberley L M; Harper, Amy M

2013-01-01

We conducted functional analyses (FA) with 4 typically developing preschool children during ongoing classroom activities and evaluated treatments that were based on FA results. Results of each child's FA suggested social-positive reinforcement functions, and differential reinforcement of alternative behavior plus time-out was effective in decreasing problem behavior and increasing appropriate behavior. We discuss the utility of classroom-based FAs and potential compromises to experimental control. © Society for the Experimental Analysis of Behavior.

Extrapolation of Functions of Many Variables by Means of Metric Analysis

NASA Astrophysics Data System (ADS)

Kryanev, Alexandr; Ivanov, Victor; Romanova, Anastasiya; Sevastianov, Leonid; Udumyan, David

2018-02-01

The paper considers a problem of extrapolating functions of several variables. It is assumed that the values of the function of m variables at a finite number of points in some domain D of the m-dimensional space are given. It is required to restore the value of the function at points outside the domain D. The paper proposes a fundamentally new method for functions of several variables extrapolation. In the presented paper, the method of extrapolating a function of many variables developed by us uses the interpolation scheme of metric analysis. To solve the extrapolation problem, a scheme based on metric analysis methods is proposed. This scheme consists of two stages. In the first stage, using the metric analysis, the function is interpolated to the points of the domain D belonging to the segment of the straight line connecting the center of the domain D with the point M, in which it is necessary to restore the value of the function. In the second stage, based on the auto regression model and metric analysis, the function values are predicted along the above straight-line segment beyond the domain D up to the point M. The presented numerical example demonstrates the efficiency of the method under consideration.

Stepwise Analysis of Differential Item Functioning Based on Multiple-Group Partial Credit Model.

ERIC Educational Resources Information Center

Muraki, Eiji

1999-01-01

Extended an Item Response Theory (IRT) method for detection of differential item functioning to the partial credit model and applied the method to simulated data using a stepwise procedure. Then applied the stepwise DIF analysis based on the multiple-group partial credit model to writing trend data from the National Assessment of Educational…

The Effects of a School-Based Functional Analysis on Subsequent Classroom Behavior

ERIC Educational Resources Information Center

Davis, Tonya N.; Durand, Shannon; Fuentes, Lisa; Dacus, Sharon; Blenden, Kara

2014-01-01

In this study we analyzed the effects of conducting a school-based functional analysis on subsequent classroom behavior. Each participant was observed in the classroom during activities that were reported by teachers to result in high levels of challenging behavior. Participants were observed during (a) baseline, prior to the administration of a…

Self-Directed Student Research through Analysis of Microarray Datasets: A Computer-Based Functional Genomics Practical Class for Masters-Level Students

ERIC Educational Resources Information Center

Grenville-Briggs, Laura J.; Stansfield, Ian

2011-01-01

This report describes a linked series of Masters-level computer practical workshops. They comprise an advanced functional genomics investigation, based upon analysis of a microarray dataset probing yeast DNA damage responses. The workshops require the students to analyse highly complex transcriptomics datasets, and were designed to stimulate…

A density difference based analysis of orbital-dependent exchange-correlation functionals

NASA Astrophysics Data System (ADS)

Grabowski, Ireneusz; Teale, Andrew M.; Fabiano, Eduardo; Śmiga, Szymon; Buksztel, Adam; Della Sala, Fabio

2014-03-01

We present a density difference based analysis for a range of orbital-dependent Kohn-Sham functionals. Results for atoms, some members of the neon isoelectronic series and small molecules are reported and compared with ab initio wave function calculations. Particular attention is paid to the quality of approximations to the exchange-only optimised effective potential (OEP) approach: we consider both the localised Hartree-Fock as well as the Krieger-Li-Iafrate methods. Analysis of density differences at the exchange-only level reveals the impact of the approximations on the resulting electronic densities. These differences are further quantified in terms of the ground state energies, frontier orbital energy differences and highest occupied orbital energies obtained. At the correlated level, an OEP approach based on a perturbative second-order correlation energy expression is shown to deliver results comparable with those from traditional wave function approaches, making it suitable for use as a benchmark against which to compare standard density functional approximations.

Structural reliability analysis under evidence theory using the active learning kriging model

NASA Astrophysics Data System (ADS)

Yang, Xufeng; Liu, Yongshou; Ma, Panke

2017-11-01

Structural reliability analysis under evidence theory is investigated. It is rigorously proved that a surrogate model providing only correct sign prediction of the performance function can meet the accuracy requirement of evidence-theory-based reliability analysis. Accordingly, a method based on the active learning kriging model which only correctly predicts the sign of the performance function is proposed. Interval Monte Carlo simulation and a modified optimization method based on Karush-Kuhn-Tucker conditions are introduced to make the method more efficient in estimating the bounds of failure probability based on the kriging model. Four examples are investigated to demonstrate the efficiency and accuracy of the proposed method.

Partial correlation-based functional connectivity analysis for functional near-infrared spectroscopy signals

NASA Astrophysics Data System (ADS)

Akın, Ata

2017-12-01

A theoretical framework, a partial correlation-based functional connectivity (PC-FC) analysis to functional near-infrared spectroscopy (fNIRS) data, is proposed. This is based on generating a common background signal from a high passed version of fNIRS data averaged over all channels as the regressor in computing the PC between pairs of channels. This approach has been employed to real data collected during a Stroop task. The results show a strong significance in the global efficiency (GE) metric computed by the PC-FC analysis for neutral, congruent, and incongruent stimuli (NS, CS, IcS; GEN=0.10±0.009, GEC=0.11±0.01, GEIC=0.13±0.015, p=0.0073). A positive correlation (r=0.729 and p=0.0259) is observed between the interference of reaction times (incongruent-neutral) and interference of GE values (GEIC-GEN) computed from [HbO] signals.

The impact of functional analysis methodology on treatment choice for self-injurious and aggressive behavior.

PubMed Central

Pelios, L; Morren, J; Tesch, D; Axelrod, S

1999-01-01

Self-injurious behavior (SIB) and aggression have been the concern of researchers because of the serious impact these behaviors have on individuals' lives. Despite the plethora of research on the treatment of SIB and aggressive behavior, the reported findings have been inconsistent regarding the effectiveness of reinforcement-based versus punishment-based procedures. We conducted a literature review to determine whether a trend could be detected in researchers' selection of reinforcement-based procedures versus punishment-based procedures, particularly since the introduction of functional analysis to behavioral assessment. The data are consistent with predictions made in the past regarding the potential impact of functional analysis methodology. Specifically, the findings indicate that, once maintaining variables for problem behavior are identified, experimenters tend to choose reinforcement-based procedures rather than punishment-based procedures as treatment for both SIB and aggressive behavior. Results indicated an increased interest in studies on the treatment of SIB and aggressive behavior, particularly since 1988. PMID:10396771

TH-EF-207A-04: A Dynamic Contrast Enhanced Cone Beam CT Technique for Evaluation of Renal Functions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Z; Shi, J; Yang, Y

Purpose: To develop a simple but robust method for the early detection and evaluation of renal functions using dynamic contrast enhanced cone beam CT technique. Methods: Experiments were performed on an integrated imaging and radiation research platform developed by our lab. Animals (n=3) were anesthetized with 20uL Ketamine/Xylazine cocktail, and then received 200uL injection of iodinated contrast agent Iopamidol via tail vein. Cone beam CT was acquired following contrast injection once per minute and up to 25 minutes. The cone beam CT was reconstructed with a dimension of 300×300×800 voxels of 130×130×130um voxel resolution. The middle kidney slices in themore » transvers and coronal planes were selected for image analysis. A double exponential function was used to fit the contrast enhanced signal intensity versus the time after contrast injection. Both pixel-based and region of interest (ROI)-based curve fitting were performed. Four parameters obtained from the curve fitting, namely the amplitude and flow constant for both contrast wash in and wash out phases, were investigated for further analysis. Results: Robust curve fitting was demonstrated for both pixel based (with R{sup 2}>0.8 for >85% pixels within the kidney contour) and ROI based (R{sup 2}>0.9 for all regions) analysis. Three different functional regions: renal pelvis, medulla and cortex, were clearly differentiated in the functional parameter map in the pixel based analysis. ROI based analysis showed the half-life T1/2 for contrast wash in and wash out phases were 0.98±0.15 and 17.04±7.16, 0.63±0.07 and 17.88±4.51, and 1.48±0.40 and 10.79±3.88 minutes for the renal pelvis, medulla and cortex, respectively. Conclusion: A robust method based on dynamic contrast enhanced cone beam CT and double exponential curve fitting has been developed to analyze the renal functions for different functional regions. Future study will be performed to investigate the sensitivity of this technique in the detection of radiation induced kidney dysfunction.« less

NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis.

PubMed

Sun, Duanchen; Liu, Yinliang; Zhang, Xiang-Sun; Wu, Ling-Yun

2017-09-21

High-throughput experimental techniques have been dramatically improved and widely applied in the past decades. However, biological interpretation of the high-throughput experimental results, such as differential expression gene sets derived from microarray or RNA-seq experiments, is still a challenging task. Gene Ontology (GO) is commonly used in the functional enrichment studies. The GO terms identified via current functional enrichment analysis tools often contain direct parent or descendant terms in the GO hierarchical structure. Highly redundant terms make users difficult to analyze the underlying biological processes. In this paper, a novel network-based probabilistic generative model, NetGen, was proposed to perform the functional enrichment analysis. An additional protein-protein interaction (PPI) network was explicitly used to assist the identification of significantly enriched GO terms. NetGen achieved a superior performance than the existing methods in the simulation studies. The effectiveness of NetGen was explored further on four real datasets. Notably, several GO terms which were not directly linked with the active gene list for each disease were identified. These terms were closely related to the corresponding diseases when accessed to the curated literatures. NetGen has been implemented in the R package CopTea publicly available at GitHub ( http://github.com/wulingyun/CopTea/ ). Our procedure leads to a more reasonable and interpretable result of the functional enrichment analysis. As a novel term combination-based functional enrichment analysis method, NetGen is complementary to current individual term-based methods, and can help to explore the underlying pathogenesis of complex diseases.

Functional Analysis and Treatment of Human-Directed Undesirable Behavior Exhibited by a Captive Chimpanzee

ERIC Educational Resources Information Center

Martin, Allison L.; Bloomsmith, Mollie A.; Kelley, Michael E.; Marr, M. Jackson; Maple, Terry L.

2011-01-01

A functional analysis identified the reinforcer maintaining feces throwing and spitting exhibited by a captive adult chimpanzee ("Pan troglodytes"). The implementation of a function-based treatment combining extinction with differential reinforcement of an alternate behavior decreased levels of inappropriate behavior. These findings further…

18 CFR 301.7 - Average System Cost methodology functionalization.

Code of Federal Regulations, 2010 CFR

2010-04-01

... SYSTEM COST METHODOLOGY FOR SALES FROM UTILITIES TO BONNEVILLE POWER ADMINISTRATION UNDER NORTHWEST POWER... functionalization under its Direct Analysis assigns costs, revenues, debits or credits based upon the actual and/or...) Functionalization methods. (1) Direct analysis, if allowed or required by Table 1, assigns costs, revenues, debits...

Classroom-Based Strategies to Incorporate Hypothesis Testing in Functional Behavior Assessments

ERIC Educational Resources Information Center

Lloyd, Blair P.; Weaver, Emily S.; Staubitz, Johanna L.

2017-01-01

When results of descriptive functional behavior assessments are unclear, hypothesis testing can help school teams understand how the classroom environment affects a student's challenging behavior. This article describes two hypothesis testing strategies that can be used in classroom settings: structural analysis and functional analysis. For each…

Functional Analyses and Treatment of Precursor Behavior

PubMed Central

Najdowski, Adel C; Wallace, Michele D; Ellsworth, Carrie L; MacAleese, Alicia N; Cleveland, Jackie M

2008-01-01

Functional analysis has been demonstrated to be an effective method to identify environmental variables that maintain problem behavior. However, there are cases when conducting functional analyses of severe problem behavior may be contraindicated. The current study applied functional analysis procedures to a class of behavior that preceded severe problem behavior (precursor behavior) and evaluated treatments based on the outcomes of the functional analyses of precursor behavior. Responding for all participants was differentiated during the functional analyses, and individualized treatments eliminated precursor behavior. These results suggest that functional analysis of precursor behavior may offer an alternative, indirect method to assess the operant function of severe problem behavior. PMID:18468282

GPU accelerated dynamic functional connectivity analysis for functional MRI data.

PubMed

Akgün, Devrim; Sakoğlu, Ünal; Esquivel, Johnny; Adinoff, Bryon; Mete, Mutlu

2015-07-01

Recent advances in multi-core processors and graphics card based computational technologies have paved the way for an improved and dynamic utilization of parallel computing techniques. Numerous applications have been implemented for the acceleration of computationally-intensive problems in various computational science fields including bioinformatics, in which big data problems are prevalent. In neuroimaging, dynamic functional connectivity (DFC) analysis is a computationally demanding method used to investigate dynamic functional interactions among different brain regions or networks identified with functional magnetic resonance imaging (fMRI) data. In this study, we implemented and analyzed a parallel DFC algorithm based on thread-based and block-based approaches. The thread-based approach was designed to parallelize DFC computations and was implemented in both Open Multi-Processing (OpenMP) and Compute Unified Device Architecture (CUDA) programming platforms. Another approach developed in this study to better utilize CUDA architecture is the block-based approach, where parallelization involves smaller parts of fMRI time-courses obtained by sliding-windows. Experimental results showed that the proposed parallel design solutions enabled by the GPUs significantly reduce the computation time for DFC analysis. Multicore implementation using OpenMP on 8-core processor provides up to 7.7× speed-up. GPU implementation using CUDA yielded substantial accelerations ranging from 18.5× to 157× speed-up once thread-based and block-based approaches were combined in the analysis. Proposed parallel programming solutions showed that multi-core processor and CUDA-supported GPU implementations accelerated the DFC analyses significantly. Developed algorithms make the DFC analyses more practical for multi-subject studies with more dynamic analyses. Copyright © 2015 Elsevier Ltd. All rights reserved.

Assignment of functional activations to probabilistic cytoarchitectonic areas revisited.

PubMed

Eickhoff, Simon B; Paus, Tomas; Caspers, Svenja; Grosbras, Marie-Helene; Evans, Alan C; Zilles, Karl; Amunts, Katrin

2007-07-01

Probabilistic cytoarchitectonic maps in standard reference space provide a powerful tool for the analysis of structure-function relationships in the human brain. While these microstructurally defined maps have already been successfully used in the analysis of somatosensory, motor or language functions, several conceptual issues in the analysis of structure-function relationships still demand further clarification. In this paper, we demonstrate the principle approaches for anatomical localisation of functional activations based on probabilistic cytoarchitectonic maps by exemplary analysis of an anterior parietal activation evoked by visual presentation of hand gestures. After consideration of the conceptual basis and implementation of volume or local maxima labelling, we comment on some potential interpretational difficulties, limitations and caveats that could be encountered. Extending and supplementing these methods, we then propose a supplementary approach for quantification of structure-function correspondences based on distribution analysis. This approach relates the cytoarchitectonic probabilities observed at a particular functionally defined location to the areal specific null distribution of probabilities across the whole brain (i.e., the full probability map). Importantly, this method avoids the need for a unique classification of voxels to a single cortical area and may increase the comparability between results obtained for different areas. Moreover, as distribution-based labelling quantifies the "central tendency" of an activation with respect to anatomical areas, it will, in combination with the established methods, allow an advanced characterisation of the anatomical substrates of functional activations. Finally, the advantages and disadvantages of the various methods are discussed, focussing on the question of which approach is most appropriate for a particular situation.

Teacher Implementation of Trial-Based Functional Analysis and Differential Reinforcement of Alternative Behavior for Students with Challenging Behavior

ERIC Educational Resources Information Center

Flynn, Susan D.; Lo, Ya-yu

2016-01-01

The purpose of this study was to examine the effects of a training package on three middle school special education teachers' accurate implementation of trial-based functional analysis (TBFA) and differential reinforcement of alternative behavior (DRA) with their students with autism spectrum disorders or emotional and behavioral disorders in the…

ANTONIA perfusion and stroke. A software tool for the multi-purpose analysis of MR perfusion-weighted datasets and quantitative ischemic stroke assessment.

PubMed

Forkert, N D; Cheng, B; Kemmling, A; Thomalla, G; Fiehler, J

2014-01-01

The objective of this work is to present the software tool ANTONIA, which has been developed to facilitate a quantitative analysis of perfusion-weighted MRI (PWI) datasets in general as well as the subsequent multi-parametric analysis of additional datasets for the specific purpose of acute ischemic stroke patient dataset evaluation. Three different methods for the analysis of DSC or DCE PWI datasets are currently implemented in ANTONIA, which can be case-specifically selected based on the study protocol. These methods comprise a curve fitting method as well as a deconvolution-based and deconvolution-free method integrating a previously defined arterial input function. The perfusion analysis is extended for the purpose of acute ischemic stroke analysis by additional methods that enable an automatic atlas-based selection of the arterial input function, an analysis of the perfusion-diffusion and DWI-FLAIR mismatch as well as segmentation-based volumetric analyses. For reliability evaluation, the described software tool was used by two observers for quantitative analysis of 15 datasets from acute ischemic stroke patients to extract the acute lesion core volume, FLAIR ratio, perfusion-diffusion mismatch volume with manually as well as automatically selected arterial input functions, and follow-up lesion volume. The results of this evaluation revealed that the described software tool leads to highly reproducible results for all parameters if the automatic arterial input function selection method is used. Due to the broad selection of processing methods that are available in the software tool, ANTONIA is especially helpful to support image-based perfusion and acute ischemic stroke research projects.

Weighted functional linear regression models for gene-based association analysis.

PubMed

Belonogova, Nadezhda M; Svishcheva, Gulnara R; Wilson, James F; Campbell, Harry; Axenovich, Tatiana I

2018-01-01

Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with P < 0.1 in at least one analysis had lower P values with weighted models. Moreover, we found an association between diastolic blood pressure and the VMP1 gene (P = 8.18×10-6), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at https://cran.r-project.org/web/packages/FREGAT/index.html.

Using a Modified Pyramidal Training Model to Teach Special Education Teachers to Conduct Trial-Based Functional Analyses

ERIC Educational Resources Information Center

Kunnavatana, S. Shanun; Bloom, Sarah E.; Samaha, Andrew L.; Lignugaris/Kraft, Benjamin; Dayton, Elizabeth; Harris, Shannon K.

2013-01-01

Functional behavioral assessments are commonly used in school settings to assess and develop interventions for problem behavior. The trial-based functional analysis is an approach that teachers can use in their classrooms to identify the function of problem behavior. The current study evaluates the effectiveness of a modified pyramidal training…

Structural landscape of base pairs containing post-transcriptional modifications in RNA

PubMed Central

Seelam, Preethi P.; Sharma, Purshotam

2017-01-01

Base pairs involving post-transcriptionally modified nucleobases are believed to play important roles in a wide variety of functional RNAs. Here we present our attempts toward understanding the structural and functional role of naturally occurring modified base pairs using a combination of X-ray crystal structure database analysis, sequence analysis, and advanced quantum chemical methods. Our bioinformatics analysis reveals that despite their presence in all major secondary structural elements, modified base pairs are most prevalent in tRNA crystal structures and most commonly involve guanine or uridine modifications. Further, analysis of tRNA sequences reveals additional examples of modified base pairs at structurally conserved tRNA regions and highlights the conservation patterns of these base pairs in three domains of life. Comparison of structures and binding energies of modified base pairs with their unmodified counterparts, using quantum chemical methods, allowed us to classify the base modifications in terms of the nature of their electronic structure effects on base-pairing. Analysis of specific structural contexts of modified base pairs in RNA crystal structures revealed several interesting scenarios, including those at the tRNA:rRNA interface, antibiotic-binding sites on the ribosome, and the three-way junctions within tRNA. These scenarios, when analyzed in the context of available experimental data, allowed us to correlate the occurrence and strength of modified base pairs with their specific functional roles. Overall, our study highlights the structural importance of modified base pairs in RNA and points toward the need for greater appreciation of the role of modified bases and their interactions, in the context of many biological processes involving RNA. PMID:28341704

Use of Model-Based Design Methods for Enhancing Resiliency Analysis of Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Knox, Lenora A.

The most common traditional non-functional requirement analysis is reliability. With systems becoming more complex, networked, and adaptive to environmental uncertainties, system resiliency has recently become the non-functional requirement analysis of choice. Analysis of system resiliency has challenges; which include, defining resilience for domain areas, identifying resilience metrics, determining resilience modeling strategies, and understanding how to best integrate the concepts of risk and reliability into resiliency. Formal methods that integrate all of these concepts do not currently exist in specific domain areas. Leveraging RAMSoS, a model-based reliability analysis methodology for Systems of Systems (SoS), we propose an extension that accounts for resiliency analysis through evaluation of mission performance, risk, and cost using multi-criteria decision-making (MCDM) modeling and design trade study variability modeling evaluation techniques. This proposed methodology, coined RAMSoS-RESIL, is applied to a case study in the multi-agent unmanned aerial vehicle (UAV) domain to investigate the potential benefits of a mission architecture where functionality to complete a mission is disseminated across multiple UAVs (distributed) opposed to being contained in a single UAV (monolithic). The case study based research demonstrates proof of concept for the proposed model-based technique and provides sufficient preliminary evidence to conclude which architectural design (distributed vs. monolithic) is most resilient based on insight into mission resilience performance, risk, and cost in addition to the traditional analysis of reliability.

Differentiating sex and species of Western Grebes (Aechmophorus occidentalis) and Clark's Grebes (Aechmophorus clarkii) and their eggs using external morphometrics and discriminant function analysis

USGS Publications Warehouse

Hartman, C. Alex; Ackerman, Joshua T.; Eagles-Smith, Collin A.; Herzog, Mark

2016-01-01

In birds where males and females are similar in size and plumage, sex determination by alternative means is necessary. Discriminant function analysis based on external morphometrics was used to distinguish males from females in two closely related species: Western Grebe (Aechmophorus occidentalis) and Clark's Grebe (A. clarkii). Additionally, discriminant function analysis was used to evaluate morphometric divergence between Western and Clark's grebe adults and eggs. Aechmophorus grebe adults (n = 576) and eggs (n = 130) were sampled across 29 lakes and reservoirs throughout California, USA, and adult sex was determined using molecular analysis. Both Western and Clark's grebes exhibited considerable sexual size dimorphism. Males averaged 6–26% larger than females among seven morphological measurements, with the greatest sexual size dimorphism occurring for bill morphometrics. Discriminant functions based on bill length, bill depth, and short tarsus length correctly assigned sex to 98% of Western Grebes, and a function based on bill length and bill depth correctly assigned sex to 99% of Clark's Grebes. Further, a simplified discriminant function based only on bill depth correctly assigned sex to 96% of Western Grebes and 98% of Clark's Grebes. In contrast, external morphometrics were not suitable for differentiating between Western and Clark's grebe adults or their eggs, with correct classification rates of discriminant functions of only 60%, 63%, and 61% for adult males, adult females, and eggs, respectively. Our results indicate little divergence in external morphology between species of Aechmophorus grebes, and instead separation is much greater between males and females.

Setting Standards for Medically-Based Running Analysis

PubMed Central

Vincent, Heather K.; Herman, Daniel C.; Lear-Barnes, Leslie; Barnes, Robert; Chen, Cong; Greenberg, Scott; Vincent, Kevin R.

2015-01-01

Setting standards for medically based running analyses is necessary to ensure that runners receive a high-quality service from practitioners. Medical and training history, physical and functional tests, and motion analysis of running at self-selected and faster speeds are key features of a comprehensive analysis. Self-reported history and movement symmetry are critical factors that require follow-up therapy or long-term management. Pain or injury is typically the result of a functional deficit above or below the site along the kinematic chain. PMID:25014394

Functional Connectivity Parcellation of the Human Thalamus by Independent Component Analysis.

PubMed

Zhang, Sheng; Li, Chiang-Shan R

2017-11-01

As a key structure to relay and integrate information, the thalamus supports multiple cognitive and affective functions through the connectivity between its subnuclei and cortical and subcortical regions. Although extant studies have largely described thalamic regional functions in anatomical terms, evidence accumulates to suggest a more complex picture of subareal activities and connectivities of the thalamus. In this study, we aimed to parcellate the thalamus and examine whole-brain connectivity of its functional clusters. With resting state functional magnetic resonance imaging data from 96 adults, we used independent component analysis (ICA) to parcellate the thalamus into 10 components. On the basis of the independence assumption, ICA helps to identify how subclusters overlap spatially. Whole brain functional connectivity of each subdivision was computed for independent component's time course (ICtc), which is a unique time series to represent an IC. For comparison, we computed seed-region-based functional connectivity using the averaged time course across all voxels within a thalamic subdivision. The results showed that, at p < 10 -6 , corrected, 49% of voxels on average overlapped among subdivisions. Compared with seed-region analysis, ICtc analysis revealed patterns of connectivity that were more distinguished between thalamic clusters. ICtc analysis demonstrated thalamic connectivity to the primary motor cortex, which has eluded the analysis as well as previous studies based on averaged time series, and clarified thalamic connectivity to the hippocampus, caudate nucleus, and precuneus. The new findings elucidate functional organization of the thalamus and suggest that ICA clustering in combination with ICtc rather than seed-region analysis better distinguishes whole-brain connectivities among functional clusters of a brain region.

Towards tests of quark-hadron duality with functional analysis and spectral function data

NASA Astrophysics Data System (ADS)

Boito, Diogo; Caprini, Irinel

2017-04-01

The presence of terms that violate quark-hadron duality in the expansion of QCD Green's functions is a generally accepted fact. Recently, a new approach was proposed for the study of duality violations (DVs), which exploits the existence of a rigorous lower bound on the functional distance, measured in a certain norm, between a "true" correlator and its approximant calculated theoretically along a contour in the complex energy plane. In the present paper, we pursue the investigation of functional-analysis-based tests towards their application to real spectral function data. We derive a closed analytic expression for the minimal functional distance based on the general weighted L2 norm and discuss its relation with the distance measured in the L∞ norm. Using fake data sets obtained from a realistic toy model in which we allow for covariances inspired from the publicly available ALEPH spectral functions, we obtain, by Monte Carlo simulations, the statistical distribution of the strength parameter that measures the magnitude of the DV term added to the usual operator product expansion. The results show that, if the region with large errors near the end point of the spectrum in τ decays is excluded, the functional-analysis-based tests using either L2 or L∞ norms are able to detect, in a statistically significant way, the presence of DVs in realistic spectral function pseudodata.

Do Functional Behavioral Assessments Improve Intervention Effectiveness for Students with ADHD? A Single-Subject Meta-Analysis

ERIC Educational Resources Information Center

Miller, Faith G.

2011-01-01

The primary purpose of this quantitative synthesis of single-subject research was to investigate the relative effectiveness of function-based and non-function-based behavioral interventions for students diagnosed with attention-deficit/hyperactivity disorder. In addition, associations between various participant, assessment, and intervention…

Learning Time-Varying Coverage Functions

PubMed Central

Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

2015-01-01

Coverage functions are an important class of discrete functions that capture the law of diminishing returns arising naturally from applications in social network analysis, machine learning, and algorithmic game theory. In this paper, we propose a new problem of learning time-varying coverage functions, and develop a novel parametrization of these functions using random features. Based on the connection between time-varying coverage functions and counting processes, we also propose an efficient parameter learning algorithm based on likelihood maximization, and provide a sample complexity analysis. We applied our algorithm to the influence function estimation problem in information diffusion in social networks, and show that with few assumptions about the diffusion processes, our algorithm is able to estimate influence significantly more accurately than existing approaches on both synthetic and real world data. PMID:25960624

Learning Time-Varying Coverage Functions.

PubMed

Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

2014-12-08

Coverage functions are an important class of discrete functions that capture the law of diminishing returns arising naturally from applications in social network analysis, machine learning, and algorithmic game theory. In this paper, we propose a new problem of learning time-varying coverage functions, and develop a novel parametrization of these functions using random features. Based on the connection between time-varying coverage functions and counting processes, we also propose an efficient parameter learning algorithm based on likelihood maximization, and provide a sample complexity analysis. We applied our algorithm to the influence function estimation problem in information diffusion in social networks, and show that with few assumptions about the diffusion processes, our algorithm is able to estimate influence significantly more accurately than existing approaches on both synthetic and real world data.

Intelligent Operation and Maintenance of Micro-grid Technology and System Development

NASA Astrophysics Data System (ADS)

Fu, Ming; Song, Jinyan; Zhao, Jingtao; Du, Jian

2018-01-01

In order to achieve the micro-grid operation and management, Studying the micro-grid operation and maintenance knowledge base. Based on the advanced Petri net theory, the fault diagnosis model of micro-grid is established, and the intelligent diagnosis and analysis method of micro-grid fault is put forward. Based on the technology, the functional system and architecture of the intelligent operation and maintenance system of micro-grid are studied, and the microcomputer fault diagnosis function is introduced in detail. Finally, the system is deployed based on the micro-grid of a park, and the micro-grid fault diagnosis and analysis is carried out based on the micro-grid operation. The system operation and maintenance function interface is displayed, which verifies the correctness and reliability of the system.

Transcriptome-wide analysis of WRKY transcription factors in wheat and their leaf rust responsive expression profiling.

PubMed

Satapathy, Lopamudra; Singh, Dharmendra; Ranjan, Prashant; Kumar, Dhananjay; Kumar, Manish; Prabhu, Kumble Vinod; Mukhopadhyay, Kunal

2014-12-01

WRKY, a plant-specific transcription factor family, has important roles in pathogen defense, abiotic cues and phytohormone signaling, yet little is known about their roles and molecular mechanism of function in response to rust diseases in wheat. We identified 100 TaWRKY sequences using wheat Expressed Sequence Tag database of which 22 WRKY sequences were novel. Identified proteins were characterized based on their zinc finger motifs and phylogenetic analysis clustered them into six clades consisting of class IIc and class III WRKY proteins. Functional annotation revealed major functions in metabolic and cellular processes in control plants; whereas response to stimuli, signaling and defense in pathogen inoculated plants, their major molecular function being binding to DNA. Tag-based expression analysis of the identified genes revealed differential expression between mock and Puccinia triticina inoculated wheat near isogenic lines. Gene expression was also performed with six rust-related microarray experiments at Gene Expression Omnibus database. TaWRKY10, 15, 17 and 56 were common in both tag-based and microarray-based differential expression analysis and could be representing rust specific WRKY genes. The obtained results will bestow insight into the functional characterization of WRKY transcription factors responsive to leaf rust pathogenesis that can be used as candidate genes in molecular breeding programs to improve biotic stress tolerance in wheat.

miRNet - dissecting miRNA-target interactions and functional associations through network-based visual analysis

PubMed Central

Fan, Yannan; Siklenka, Keith; Arora, Simran K.; Ribeiro, Paula; Kimmins, Sarah; Xia, Jianguo

2016-01-01

MicroRNAs (miRNAs) can regulate nearly all biological processes and their dysregulation is implicated in various complex diseases and pathological conditions. Recent years have seen a growing number of functional studies of miRNAs using high-throughput experimental technologies, which have produced a large amount of high-quality data regarding miRNA target genes and their interactions with small molecules, long non-coding RNAs, epigenetic modifiers, disease associations, etc. These rich sets of information have enabled the creation of comprehensive networks linking miRNAs with various biologically important entities to shed light on their collective functions and regulatory mechanisms. Here, we introduce miRNet, an easy-to-use web-based tool that offers statistical, visual and network-based approaches to help researchers understand miRNAs functions and regulatory mechanisms. The key features of miRNet include: (i) a comprehensive knowledge base integrating high-quality miRNA-target interaction data from 11 databases; (ii) support for differential expression analysis of data from microarray, RNA-seq and quantitative PCR; (iii) implementation of a flexible interface for data filtering, refinement and customization during network creation; (iv) a powerful fully featured network visualization system coupled with enrichment analysis. miRNet offers a comprehensive tool suite to enable statistical analysis and functional interpretation of various data generated from current miRNA studies. miRNet is freely available at http://www.mirnet.ca. PMID:27105848

Gene context analysis in the Integrated Microbial Genomes (IMG) data management system.

PubMed

Mavromatis, Konstantinos; Chu, Ken; Ivanova, Natalia; Hooper, Sean D; Markowitz, Victor M; Kyrpides, Nikos C

2009-11-24

Computational methods for determining the function of genes in newly sequenced genomes have been traditionally based on sequence similarity to genes whose function has been identified experimentally. Function prediction methods can be extended using gene context analysis approaches such as examining the conservation of chromosomal gene clusters, gene fusion events and co-occurrence profiles across genomes. Context analysis is based on the observation that functionally related genes are often having similar gene context and relies on the identification of such events across phylogenetically diverse collection of genomes. We have used the data management system of the Integrated Microbial Genomes (IMG) as the framework to implement and explore the power of gene context analysis methods because it provides one of the largest available genome integrations. Visualization and search tools to facilitate gene context analysis have been developed and applied across all publicly available archaeal and bacterial genomes in IMG. These computations are now maintained as part of IMG's regular genome content update cycle. IMG is available at: http://img.jgi.doe.gov.

Neurobiological changes of schizotypy: evidence from both volume-based morphometric analysis and resting-state functional connectivity.

PubMed

Wang, Yi; Yan, Chao; Yin, Da-zhi; Fan, Ming-xia; Cheung, Eric F C; Pantelis, Christos; Chan, Raymond C K

2015-03-01

The current study sought to examine the underlying brain changes in individuals with high schizotypy by integrating networks derived from brain structural and functional imaging. Individuals with high schizotypy (n = 35) and low schizotypy (n = 34) controls were screened using the Schizotypal Personality Questionnaire and underwent brain structural and resting-state functional magnetic resonance imaging on a 3T scanner. Voxel-based morphometric analysis and graph theory-based functional network analysis were conducted. Individuals with high schizotypy showed reduced gray matter (GM) density in the insula and the dorsolateral prefrontal gyrus. The graph theoretical analysis showed that individuals with high schizotypy showed similar global properties in their functional networks as low schizotypy individuals. Several hubs of the functional network were identified in both groups, including the insula, the lingual gyrus, the postcentral gyrus, and the rolandic operculum. More hubs in the frontal lobe and fewer hubs in the occipital lobe were identified in individuals with high schizotypy. By comparing the functional connectivity between clusters with abnormal GM density and the whole brain, individuals with high schizotypy showed weaker functional connectivity between the left insula and the putamen, but stronger connectivity between the cerebellum and the medial frontal gyrus. Taken together, our findings suggest that individuals with high schizotypy present changes in terms of GM and resting-state functional connectivity, especially in the frontal lobe. © The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Subphenotypes of mild-to-moderate COPD by factor and cluster analysis of pulmonary function, CT imaging and breathomics in a population-based survey.

PubMed

Fens, Niki; van Rossum, Annelot G J; Zanen, Pieter; van Ginneken, Bram; van Klaveren, Rob J; Zwinderman, Aeilko H; Sterk, Peter J

2013-06-01

Classification of COPD is currently based on the presence and severity of airways obstruction. However, this may not fully reflect the phenotypic heterogeneity of COPD in the (ex-) smoking community. We hypothesized that factor analysis followed by cluster analysis of functional, clinical, radiological and exhaled breath metabolomic features identifies subphenotypes of COPD in a community-based population of heavy (ex-) smokers. Adults between 50-75 years with a smoking history of at least 15 pack-years derived from a random population-based survey as part of the NELSON study underwent detailed assessment of pulmonary function, chest CT scanning, questionnaires and exhaled breath molecular profiling using an electronic nose. Factor and cluster analyses were performed on the subgroup of subjects fulfilling the GOLD criteria for COPD (post-BD FEV1/FVC < 0.70). Three hundred subjects were recruited, of which 157 fulfilled the criteria for COPD and were included in the factor and cluster analysis. Four clusters were identified: cluster 1 (n = 35; 22%): mild COPD, limited symptoms and good quality of life. Cluster 2 (n = 48; 31%): low lung function, combined emphysema and chronic bronchitis and a distinct breath molecular profile. Cluster 3 (n = 60; 38%): emphysema predominant COPD with preserved lung function. Cluster 4 (n = 14; 9%): highly symptomatic COPD with mildly impaired lung function. In a leave-one-out validation analysis an accuracy of 97.4% was reached. This unbiased taxonomy for mild to moderate COPD reinforces clusters found in previous studies and thereby allows better phenotyping of COPD in the general (ex-) smoking population.

Projection-based motion estimation for cardiac functional analysis with high temporal resolution: a proof-of-concept study with digital phantom experiment

NASA Astrophysics Data System (ADS)

Suzuki, Yuki; Fung, George S. K.; Shen, Zeyang; Otake, Yoshito; Lee, Okkyun; Ciuffo, Luisa; Ashikaga, Hiroshi; Sato, Yoshinobu; Taguchi, Katsuyuki

2017-03-01

Cardiac motion (or functional) analysis has shown promise not only for non-invasive diagnosis of cardiovascular diseases but also for prediction of cardiac future events. Current imaging modalities has limitations that could degrade the accuracy of the analysis indices. In this paper, we present a projection-based motion estimation method for x-ray CT that estimates cardiac motion with high spatio-temporal resolution using projection data and a reference 3D volume image. The experiment using a synthesized digital phantom showed promising results for motion analysis.

Oak Ridge Environmental Information System (OREIS) functional system design document

DOE Office of Scientific and Technical Information (OSTI.GOV)

Birchfield, T.E.; Brown, M.O.; Coleman, P.R.

1994-03-01

The OREIS Functional System Design document provides a detailed functional description of the Oak Ridge Environmental Information System (OREIS). It expands the system requirements defined in the OREIS Phase 1-System Definition Document (ES/ER/TM-34). Documentation of OREIS development is based on the Automated Data Processing System Development Methodology, a Martin Marietta Energy Systems, Inc., procedure written to assist in developing scientific and technical computer systems. This document focuses on the development of the functional design of the user interface, which includes the integration of commercial applications software. The data model and data dictionary are summarized briefly; however, the Data Management Planmore » for OREIS (ES/ER/TM-39), a companion document to the Functional System Design document, provides the complete data dictionary and detailed descriptions of the requirements for the data base structure. The OREIS system will provide the following functions, which are executed from a Menu Manager: (1) preferences, (2) view manager, (3) macro manager, (4) data analysis (assisted analysis and unassisted analysis), and (5) spatial analysis/map generation (assisted ARC/INFO and unassisted ARC/INFO). Additional functionality includes interprocess communications, which handle background operations of OREIS.« less

Spatial Assessment of Forest Ecosystem Functions and Services using Human Relating Factors for SDG

NASA Astrophysics Data System (ADS)

Song, C.; Lee, W. K.; Jeon, S. W.; Kim, T.; Lim, C. H.

2015-12-01

Application of ecosystem service concept in environmental related decision making could be numerical and objective standard for policy maker between preserving and developing perspective of environment. However, pursuing maximum benefit from natural capital through ecosystem services caused failure by losing ecosystem functions through its trade-offs. Therefore, difference between ecosystem functions and services were demonstrated and would apply human relating perspectives. Assessment results of ecosystem functions and services can be divided 3 parts. Tree growth per year set as the ecosystem function factor and indicated through so called pure function map. After that, relating functions can be driven such as water conservation, air pollutant purification, climate change regulation, and timber production. Overall process and amount are numerically quantified. These functional results can be transferred to ecosystem services by multiplying economic unit value, so function reflecting service maps can be generated. On the other hand, above services, to implement more reliable human demand, human reflecting service maps are also be developed. As the validation, quantified ecosystem functions are compared with former results through pixel based analysis. Three maps are compared, and through comparing difference between ecosystem function and services and inversed trends in function based and human based service are analysed. In this study, we could find differences in PF, FRS, and HRS in relation to based ecosystem conditions. This study suggests that the differences in PF, FRS, and HRS should be understood in the decision making process for sustainable management of ecosystem services. Although the analysis is based on in sort existing process separation, it is important to consider the possibility of different usage of ecosystem function assessment results and ecosystem service assessment results in SDG policy making. Furthermore, process based functional approach can suggest environmental information which is reflected the other kinds of perspective.

Genome-wide protein-protein interactions and protein function exploration in cyanobacteria

PubMed Central

Lv, Qi; Ma, Weimin; Liu, Hui; Li, Jiang; Wang, Huan; Lu, Fang; Zhao, Chen; Shi, Tieliu

2015-01-01

Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and “interologs” in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria. PMID:26490033

Temporal Drivers of Liking Based on Functional Data Analysis and Non-Additive Models for Multi-Attribute Time-Intensity Data of Fruit Chews.

PubMed

Kuesten, Carla; Bi, Jian

2018-06-03

Conventional drivers of liking analysis was extended with a time dimension into temporal drivers of liking (TDOL) based on functional data analysis methodology and non-additive models for multiple-attribute time-intensity (MATI) data. The non-additive models, which consider both direct effects and interaction effects of attributes to consumer overall liking, include Choquet integral and fuzzy measure in the multi-criteria decision-making, and linear regression based on variance decomposition. Dynamics of TDOL, i.e., the derivatives of the relative importance functional curves were also explored. Well-established R packages 'fda', 'kappalab' and 'relaimpo' were used in the paper for developing TDOL. Applied use of these methods shows that the relative importance of MATI curves offers insights for understanding the temporal aspects of consumer liking for fruit chews.

Heart failure analysis dashboard for patient's remote monitoring combining multiple artificial intelligence technologies.

PubMed

Guidi, G; Pettenati, M C; Miniati, R; Iadanza, E

2012-01-01

In this paper we describe an Heart Failure analysis Dashboard that, combined with a handy device for the automatic acquisition of a set of patient's clinical parameters, allows to support telemonitoring functions. The Dashboard's intelligent core is a Computer Decision Support System designed to assist the clinical decision of non-specialist caring personnel, and it is based on three functional parts: Diagnosis, Prognosis, and Follow-up management. Four Artificial Intelligence-based techniques are compared for providing diagnosis function: a Neural Network, a Support Vector Machine, a Classification Tree and a Fuzzy Expert System whose rules are produced by a Genetic Algorithm. State of the art algorithms are used to support a score-based prognosis function. The patient's Follow-up is used to refine the diagnosis.

GO-based functional dissimilarity of gene sets.

PubMed

Díaz-Díaz, Norberto; Aguilar-Ruiz, Jesús S

2011-09-01

The Gene Ontology (GO) provides a controlled vocabulary for describing the functions of genes and can be used to evaluate the functional coherence of gene sets. Many functional coherence measures consider each pair of gene functions in a set and produce an output based on all pairwise distances. A single gene can encode multiple proteins that may differ in function. For each functionality, other proteins that exhibit the same activity may also participate. Therefore, an identification of the most common function for all of the genes involved in a biological process is important in evaluating the functional similarity of groups of genes and a quantification of functional coherence can helps to clarify the role of a group of genes working together. To implement this approach to functional assessment, we present GFD (GO-based Functional Dissimilarity), a novel dissimilarity measure for evaluating groups of genes based on the most relevant functions of the whole set. The measure assigns a numerical value to the gene set for each of the three GO sub-ontologies. Results show that GFD performs robustly when applied to gene set of known functionality (extracted from KEGG). It performs particularly well on randomly generated gene sets. An ROC analysis reveals that the performance of GFD in evaluating the functional dissimilarity of gene sets is very satisfactory. A comparative analysis against other functional measures, such as GS2 and those presented by Resnik and Wang, also demonstrates the robustness of GFD.

A confirmative clinimetric analysis of the 36-item Family Assessment Device.

PubMed

Timmerby, Nina; Cosci, Fiammetta; Watson, Maggie; Csillag, Claudio; Schmitt, Florence; Steck, Barbara; Bech, Per; Thastum, Mikael

2018-02-07

The Family Assessment Device (FAD) is a 60-item questionnaire widely used to evaluate self-reported family functioning. However, the factor structure as well as the number of items has been questioned. A shorter and more user-friendly version of the original FAD-scale, the 36-item FAD, has therefore previously been proposed, based on findings in a nonclinical population of adults. We aimed in this study to evaluate the brief 36-item version of the FAD in a clinical population. Data from a European multinational study, examining factors associated with levels of family functioning in adult cancer patients' families, were used. Both healthy and ill parents completed the 60-item version FAD. The psychometric analyses conducted were Principal Component Analysis and Mokken-analysis. A total of 564 participants were included. Based on the psychometric analysis we confirmed that the 36-item version of the FAD has robust psychometric properties and can be used in clinical populations. The present analysis confirmed that the 36-item version of the FAD (18 items assessing 'well-being' and 18 items assessing 'dysfunctional' family function) is a brief scale where the summed total score is a valid measure of the dimensions of family functioning. This shorter version of the FAD is, in accordance with the concept of 'measurement-based care', an easy to use scale that could be considered when the aim is to evaluate self-reported family functioning.

Application of a data-mining method based on Bayesian networks to lesion-deficit analysis

NASA Technical Reports Server (NTRS)

Herskovits, Edward H.; Gerring, Joan P.

2003-01-01

Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.

Effectiveness of Circuit-Based Exercises on Gait Speed, Balance, and Functional Mobility in People Affected by Stroke: A Meta-Analysis.

PubMed

Bonini-Rocha, Ana Clara; de Andrade, Anderson Lúcio Souza; Moraes, André Marques; Gomide Matheus, Liana Barbaresco; Diniz, Leonardo Rios; Martins, Wagner Rodrigues

2018-04-01

Several interventions have been proposed to rehabilitate patients with neurologic dysfunctions due to stroke. However, the effectiveness of circuit-based exercises according to its actual definition, ie, an overall program to improve strength, stamina, balance or functioning, was not provided. To examine the effectiveness of circuit-based exercise in the treatment of people affected by stroke. A search through PubMed, Embase, Cochrane Library, and Physiotherapy Evidence Database databases was performed to identify controlled clinical trials without language or date restriction. The overall mean difference with 95% confidence interval was calculated for all outcomes. Two independent reviewers assessed the risk of bias. Eleven studies met the inclusion criteria, and 8 presented suitable data to perform a meta-analysis. Quantitative analysis showed that circuit-based exercise was more effective than conventional intervention on gait speed (mean difference of 0.11 m/s) and circuit-based exercise was not significantly more effective than conventional intervention on balance and functional mobility. Our results demonstrated that circuit-based exercise presents better effects on gait when compared with conventional intervention and that its effects on balance and functional mobility were not better than conventional interventions. I. Copyright © 2018 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

Implementation and Validation of Trial-Based Functional Analyses in Public Elementary School Settings

ERIC Educational Resources Information Center

Lloyd, Blair P.; Wehby, Joseph H.; Weaver, Emily S.; Goldman, Samantha E.; Harvey, Michelle N.; Sherlock, Daniel R.

2015-01-01

Although functional analysis (FA) remains the standard for identifying the function of problem behavior for students with developmental disabilities, traditional FA procedures are typically costly in terms of time, resources, and perceived risks. Preliminary research suggests that trial-based FA may be a less costly alternative. The purpose of…

Do Functional Behavioral Assessments Improve Intervention Effectiveness for Students Diagnosed with ADHD? A Single-Subject Meta-Analysis

ERIC Educational Resources Information Center

Miller, Faith G.; Lee, David L.

2013-01-01

The primary purpose of this quantitative synthesis of single-subject research was to investigate the relative effectiveness of function-based and non-function-based behavioral interventions for students diagnosed with attention-deficit/hyperactivity disorder. In addition, associations between various participant, assessment, and intervention…

Functional Groups Based on Leaf Physiology: Are they Spatially and Temporally Robust?

NASA Technical Reports Server (NTRS)

Foster, Tammy E.; Brooks, J. Renee

2004-01-01

The functional grouping hypothesis, which suggests that complexity in ecosystem function can be simplified by grouping species with similar responses, was tested in the Florida scrub habitat. Functional groups were identified based on how species in fire maintained Florida scrub regulate exchange of carbon and water with the atmosphere as indicated by both instantaneous gas exchange measurements and integrated measures of function (%N, delta C-13, delta N-15, C-N ratio). Using cluster analysis, five distinct physiologically-based functional groups were identified in the fire maintained scrub. These functional groups were tested to determine if they were robust spatially, temporally, and with management regime. Analysis of Similarities (ANOSIM), a non-parametric multivariate analysis, indicated that these five physiologically-based groupings were not altered by plot differences (R = -0.115, p = 0.893) or by the three different management regimes; prescribed burn, mechanically treated and burn, and fire-suppressed (R = 0.018, p = 0.349). The physiological groupings also remained robust between the two climatically different years 1999 and 2000 (R = -0.027, p = 0.725). Easy-to-measure morphological characteristics indicating functional groups would be more practical for scaling and modeling ecosystem processes than detailed gas-exchange measurements, therefore we tested a variety of morphological characteristics as functional indicators. A combination of non-parametric multivariate techniques (Hierarchical cluster analysis, non-metric Multi-Dimensional Scaling, and ANOSIM) were used to compare the ability of life form, leaf thickness, and specific leaf area classifications to identify the physiologically-based functional groups. Life form classifications (ANOSIM; R = 0.629, p 0.001) were able to depict the physiological groupings more adequately than either specific leaf area (ANOSIM; R = 0.426, p = 0.001) or leaf thickness (ANOSIM; R 0.344, p 0.001). The ability of life forms to depict the physiological groupings was improved by separating the parasitic Ximenia americana from the shrub category (ANOSIM; R = 0.794, p = 0.001). Therefore, a life form classification including parasites was determined to be a good indicator of the physiological processes of scrub species, and would be a useful method of grouping for scaling physiological processes to the ecosystem level.

Resting-State Seed-Based Analysis: An Alternative to Task-Based Language fMRI and Its Laterality Index.

PubMed

Smitha, K A; Arun, K M; Rajesh, P G; Thomas, B; Kesavadas, C

2017-06-01

Language is a cardinal function that makes human unique. Preservation of language function poses a great challenge for surgeons during resection. The aim of the study was to assess the efficacy of resting-state fMRI in the lateralization of language function in healthy subjects to permit its further testing in patients who are unable to perform task-based fMRI. Eighteen healthy right-handed volunteers were prospectively evaluated with resting-state fMRI and task-based fMRI to assess language networks. The laterality indices of Broca and Wernicke areas were calculated by using task-based fMRI via a voxel-value approach. We adopted seed-based resting-state fMRI connectivity analysis together with parameters such as amplitude of low-frequency fluctuation and fractional amplitude of low-frequency fluctuation (fALFF). Resting-state fMRI connectivity maps for language networks were obtained from Broca and Wernicke areas in both hemispheres. We performed correlation analysis between the laterality index and the z scores of functional connectivity, amplitude of low-frequency fluctuation, and fALFF. Pearson correlation analysis between signals obtained from the z score of fALFF and the laterality index yielded a correlation coefficient of 0.849 ( P < .05). Regression analysis of the fALFF with the laterality index yielded an R 2 value of 0.721, indicating that 72.1% of the variance in the laterality index of task-based fMRI could be predicted from the fALFF of resting-state fMRI. The present study demonstrates that fALFF can be used as an alternative to task-based fMRI for assessing language laterality. There was a strong positive correlation between the fALFF of the Broca area of resting-state fMRI with the laterality index of task-based fMRI. Furthermore, we demonstrated the efficacy of fALFF for predicting the laterality of task-based fMRI. © 2017 by American Journal of Neuroradiology.

Toward a revised evolutionary adaptationist analysis of depression: the social navigation hypothesis.

PubMed

Watson, Paul J; Andrews, Paul W

2002-10-01

Evolutionary biologists use Darwinian theory and functional design ("reverse engineering") analyses, to develop and test hypotheses about the adaptive functions of traits. Based upon a consideration of human social life and a functional design analysis of depression's core symptomatology we offer a comprehensive theory of its adaptive significance called the Social Navigation Hypothesis (SNH). The SNH attempts to account for all intensities of depression based on standard evolutionary theories of sociality, communication and psychological pain. The SNH suggests that depression evolved to perform two complimentary social problem-solving functions. First, depression induces cognitive changes that focus and enhance capacities for the accurate analysis and solution of key social problems, suggesting a social rumination function. Second, the costs associated with the anhedonia and psychomotor perturbation of depression can persuade reluctant social partners to provide help or make concessions via two possible mechanisms, namely, honest signaling and passive, unintentional fitness extortion. Thus it may also have a social motivation function.

Model-Based Segmentation of Cortical Regions of Interest for Multi-subject Analysis of fMRI Data

NASA Astrophysics Data System (ADS)

Engel, Karin; Brechmann, Andr'e.; Toennies, Klaus

The high inter-subject variability of human neuroanatomy complicates the analysis of functional imaging data across subjects. We propose a method for the correct segmentation of cortical regions of interest based on the cortical surface. First results on the segmentation of Heschl's gyrus indicate the capability of our approach for correct comparison of functional activations in relation to individual cortical patterns.

Hypothesis testing in functional linear regression models with Neyman's truncation and wavelet thresholding for longitudinal data.

PubMed

Yang, Xiaowei; Nie, Kun

2008-03-15

Longitudinal data sets in biomedical research often consist of large numbers of repeated measures. In many cases, the trajectories do not look globally linear or polynomial, making it difficult to summarize the data or test hypotheses using standard longitudinal data analysis based on various linear models. An alternative approach is to apply the approaches of functional data analysis, which directly target the continuous nonlinear curves underlying discretely sampled repeated measures. For the purposes of data exploration, many functional data analysis strategies have been developed based on various schemes of smoothing, but fewer options are available for making causal inferences regarding predictor-outcome relationships, a common task seen in hypothesis-driven medical studies. To compare groups of curves, two testing strategies with good power have been proposed for high-dimensional analysis of variance: the Fourier-based adaptive Neyman test and the wavelet-based thresholding test. Using a smoking cessation clinical trial data set, this paper demonstrates how to extend the strategies for hypothesis testing into the framework of functional linear regression models (FLRMs) with continuous functional responses and categorical or continuous scalar predictors. The analysis procedure consists of three steps: first, apply the Fourier or wavelet transform to the original repeated measures; then fit a multivariate linear model in the transformed domain; and finally, test the regression coefficients using either adaptive Neyman or thresholding statistics. Since a FLRM can be viewed as a natural extension of the traditional multiple linear regression model, the development of this model and computational tools should enhance the capacity of medical statistics for longitudinal data.

Resting State Functional Connectivity in Mild Traumatic Brain Injury at the Acute Stage: Independent Component and Seed-Based Analyses

PubMed Central

Iraji, Armin; Benson, Randall R.; Welch, Robert D.; O'Neil, Brian J.; Woodard, John L.; Imran Ayaz, Syed; Kulek, Andrew; Mika, Valerie; Medado, Patrick; Soltanian-Zadeh, Hamid; Liu, Tianming; Haacke, E. Mark

2015-01-01

Abstract Mild traumatic brain injury (mTBI) accounts for more than 1 million emergency visits each year. Most of the injured stay in the emergency department for a few hours and are discharged home without a specific follow-up plan because of their negative clinical structural imaging. Advanced magnetic resonance imaging (MRI), particularly functional MRI (fMRI), has been reported as being sensitive to functional disturbances after brain injury. In this study, a cohort of 12 patients with mTBI were prospectively recruited from the emergency department of our local Level-1 trauma center for an advanced MRI scan at the acute stage. Sixteen age- and sex-matched controls were also recruited for comparison. Both group-based and individual-based independent component analysis of resting-state fMRI (rsfMRI) demonstrated reduced functional connectivity in both posterior cingulate cortex (PCC) and precuneus regions in comparison with controls, which is part of the default mode network (DMN). Further seed-based analysis confirmed reduced functional connectivity in these two regions and also demonstrated increased connectivity between these regions and other regions of the brain in mTBI. Seed-based analysis using the thalamus, hippocampus, and amygdala regions further demonstrated increased functional connectivity between these regions and other regions of the brain, particularly in the frontal lobe, in mTBI. Our data demonstrate alterations of multiple brain networks at the resting state, particularly increased functional connectivity in the frontal lobe, in response to brain concussion at the acute stage. Resting-state functional connectivity of the DMN could serve as a potential biomarker for improved detection of mTBI in the acute setting. PMID:25285363

Functional connectivity analysis of the neural bases of emotion regulation: A comparison of independent component method with density-based k-means clustering method.

PubMed

Zou, Ling; Guo, Qian; Xu, Yi; Yang, Biao; Jiao, Zhuqing; Xiang, Jianbo

2016-04-29

Functional magnetic resonance imaging (fMRI) is an important tool in neuroscience for assessing connectivity and interactions between distant areas of the brain. To find and characterize the coherent patterns of brain activity as a means of identifying brain systems for the cognitive reappraisal of the emotion task, both density-based k-means clustering and independent component analysis (ICA) methods can be applied to characterize the interactions between brain regions involved in cognitive reappraisal of emotion. Our results reveal that compared with the ICA method, the density-based k-means clustering method provides a higher sensitivity of polymerization. In addition, it is more sensitive to those relatively weak functional connection regions. Thus, the study concludes that in the process of receiving emotional stimuli, the relatively obvious activation areas are mainly distributed in the frontal lobe, cingulum and near the hypothalamus. Furthermore, density-based k-means clustering method creates a more reliable method for follow-up studies of brain functional connectivity.

Evaluating the Accuracy of Results for Teacher Implemented Trial-Based Functional Analyses.

PubMed

Rispoli, Mandy; Ninci, Jennifer; Burke, Mack D; Zaini, Samar; Hatton, Heather; Sanchez, Lisa

2015-09-01

Trial-based functional analysis (TBFA) allows for the systematic and experimental assessment of challenging behavior in applied settings. The purposes of this study were to evaluate a professional development package focused on training three Head Start teachers to conduct TBFAs with fidelity during ongoing classroom routines. To assess the accuracy of the TBFA results, the effects of a function-based intervention derived from the TBFA were compared with the effects of a non-function-based intervention. Data were collected on child challenging behavior and appropriate communication. An A-B-A-C-D design was utilized in which A represented baseline, and B and C consisted of either function-based or non-function-based interventions counterbalanced across participants, and D represented teacher implementation of the most effective intervention. Results showed that the function-based intervention produced greater decreases in challenging behavior and greater increases in appropriate communication than the non-function-based intervention for all three children. © The Author(s) 2015.

Urban Planning and Management Information Systems Analysis and Design Based on GIS

NASA Astrophysics Data System (ADS)

Xin, Wang

Based on the analysis of existing relevant systems on the basis of inadequate, after a detailed investigation and research, urban planning and management information system will be designed for three-tier structure system, under the LAN using C/S mode architecture. Related functions for the system designed in accordance with the requirements of the architecture design of the functional relationships between the modules. Analysis of the relevant interface and design, data storage solutions proposed. The design for small and medium urban planning information system provides a viable building program.

Integration of a satellite ground support system based on analysis of the satellite ground support domain

NASA Technical Reports Server (NTRS)

Pendley, R. D.; Scheidker, E. J.; Levitt, D. S.; Myers, C. R.; Werking, R. D.

1994-01-01

This analysis defines a complete set of ground support functions based on those practiced in real space flight operations during the on-orbit phase of a mission. These functions are mapped against ground support functions currently in use by NASA and DOD. Software components to provide these functions can be hosted on RISC-based work stations and integrated to provide a modular, integrated ground support system. Such modular systems can be configured to provide as much ground support functionality as desired. This approach to ground systems has been widely proposed and prototyped both by government institutions and commercial vendors. The combined set of ground support functions we describe can be used as a standard to evaluate candidate ground systems. This approach has also been used to develop a prototype of a modular, loosely-integrated ground support system, which is discussed briefly. A crucial benefit to a potential user is that all the components are flight-qualified, thus giving high confidence in their accuracy and reliability.

Integration of a satellite ground support system based on analysis of the satellite ground support domain

NASA Astrophysics Data System (ADS)

Pendley, R. D.; Scheidker, E. J.; Levitt, D. S.; Myers, C. R.; Werking, R. D.

1994-11-01

This analysis defines a complete set of ground support functions based on those practiced in real space flight operations during the on-orbit phase of a mission. These functions are mapped against ground support functions currently in use by NASA and DOD. Software components to provide these functions can be hosted on RISC-based work stations and integrated to provide a modular, integrated ground support system. Such modular systems can be configured to provide as much ground support functionality as desired. This approach to ground systems has been widely proposed and prototyped both by government institutions and commercial vendors. The combined set of ground support functions we describe can be used as a standard to evaluate candidate ground systems. This approach has also been used to develop a prototype of a modular, loosely-integrated ground support system, which is discussed briefly. A crucial benefit to a potential user is that all the components are flight-qualified, thus giving high confidence in their accuracy and reliability.

Variance-Based Sensitivity Analysis to Support Simulation-Based Design Under Uncertainty

DOE PAGES

Opgenoord, Max M. J.; Allaire, Douglas L.; Willcox, Karen E.

2016-09-12

Sensitivity analysis plays a critical role in quantifying uncertainty in the design of engineering systems. A variance-based global sensitivity analysis is often used to rank the importance of input factors, based on their contribution to the variance of the output quantity of interest. However, this analysis assumes that all input variability can be reduced to zero, which is typically not the case in a design setting. Distributional sensitivity analysis (DSA) instead treats the uncertainty reduction in the inputs as a random variable, and defines a variance-based sensitivity index function that characterizes the relative contribution to the output variance as amore » function of the amount of uncertainty reduction. This paper develops a computationally efficient implementation for the DSA formulation and extends it to include distributions commonly used in engineering design under uncertainty. Application of the DSA method to the conceptual design of a commercial jetliner demonstrates how the sensitivity analysis provides valuable information to designers and decision-makers on where and how to target uncertainty reduction efforts.« less

Variance-Based Sensitivity Analysis to Support Simulation-Based Design Under Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Opgenoord, Max M. J.; Allaire, Douglas L.; Willcox, Karen E.

Sensitivity analysis plays a critical role in quantifying uncertainty in the design of engineering systems. A variance-based global sensitivity analysis is often used to rank the importance of input factors, based on their contribution to the variance of the output quantity of interest. However, this analysis assumes that all input variability can be reduced to zero, which is typically not the case in a design setting. Distributional sensitivity analysis (DSA) instead treats the uncertainty reduction in the inputs as a random variable, and defines a variance-based sensitivity index function that characterizes the relative contribution to the output variance as amore » function of the amount of uncertainty reduction. This paper develops a computationally efficient implementation for the DSA formulation and extends it to include distributions commonly used in engineering design under uncertainty. Application of the DSA method to the conceptual design of a commercial jetliner demonstrates how the sensitivity analysis provides valuable information to designers and decision-makers on where and how to target uncertainty reduction efforts.« less

Structure-Based Phylogenetic Analysis of the Lipocalin Superfamily.

PubMed

Lakshmi, Balasubramanian; Mishra, Madhulika; Srinivasan, Narayanaswamy; Archunan, Govindaraju

2015-01-01

Lipocalins constitute a superfamily of extracellular proteins that are found in all three kingdoms of life. Although very divergent in their sequences and functions, they show remarkable similarity in 3-D structures. Lipocalins bind and transport small hydrophobic molecules. Earlier sequence-based phylogenetic studies of lipocalins highlighted that they have a long evolutionary history. However the molecular and structural basis of their functional diversity is not completely understood. The main objective of the present study is to understand functional diversity of the lipocalins using a structure-based phylogenetic approach. The present study with 39 protein domains from the lipocalin superfamily suggests that the clusters of lipocalins obtained by structure-based phylogeny correspond well with the functional diversity. The detailed analysis on each of the clusters and sub-clusters reveals that the 39 lipocalin domains cluster based on their mode of ligand binding though the clustering was performed on the basis of gross domain structure. The outliers in the phylogenetic tree are often from single member families. Also structure-based phylogenetic approach has provided pointers to assign putative function for the domains of unknown function in lipocalin family. The approach employed in the present study can be used in the future for the functional identification of new lipocalin proteins and may be extended to other protein families where members show poor sequence similarity but high structural similarity.

Structural and functional changes in the somatosensory cortex in euthymic females with bipolar disorder.

PubMed

Minuzzi, Luciano; Syan, Sabrina K; Smith, Mara; Hall, Alexander; Hall, Geoffrey Bc; Frey, Benicio N

2017-12-01

Current evidence from neuroimaging data suggests possible dysfunction of the fronto-striatal-limbic circuits in individuals with bipolar disorder. Somatosensory cortical function has been implicated in emotional recognition, risk-taking and affective responses through sensory modalities. This study investigates anatomy and function of the somatosensory cortex in euthymic bipolar women. In total, 68 right-handed euthymic women (bipolar disorder = 32 and healthy controls = 36) between 16 and 45 years of age underwent high-resolution anatomical and functional magnetic resonance imaging during the mid-follicular menstrual phase. The somatosensory cortex was used as a seed region for resting-state functional connectivity analysis. Voxel-based morphometry was used to evaluate somatosensory cortical gray matter volume between groups. We found increased resting-state functional connectivity between the somatosensory cortex and insular cortex, inferior prefrontal gyrus and frontal orbital cortex in euthymic bipolar disorder subjects compared to healthy controls. Voxel-based morphometry analysis showed decreased gray matter in the left somatosensory cortex in the bipolar disorder group. Whole-brain voxel-based morphometry analysis controlled by age did not reveal any additional significant difference between groups. This study is the first to date to evaluate anatomy and function of the somatosensory cortex in a well-characterized sample of euthymic bipolar disorder females. Anatomical and functional changes in the somatosensory cortex in this population might contribute to the pathophysiology of bipolar disorder.

Obesity as a risk factor for developing functional limitation among older adults: A conditional inference tree analysis.

PubMed

Cheng, Feon W; Gao, Xiang; Bao, Le; Mitchell, Diane C; Wood, Craig; Sliwinski, Martin J; Smiciklas-Wright, Helen; Still, Christopher D; Rolston, David D K; Jensen, Gordon L

2017-07-01

To examine the risk factors of developing functional decline and make probabilistic predictions by using a tree-based method that allows higher order polynomials and interactions of the risk factors. The conditional inference tree analysis, a data mining approach, was used to construct a risk stratification algorithm for developing functional limitation based on BMI and other potential risk factors for disability in 1,951 older adults without functional limitations at baseline (baseline age 73.1 ± 4.2 y). We also analyzed the data with multivariate stepwise logistic regression and compared the two approaches (e.g., cross-validation). Over a mean of 9.2 ± 1.7 years of follow-up, 221 individuals developed functional limitation. Higher BMI, age, and comorbidity were consistently identified as significant risk factors for functional decline using both methods. Based on these factors, individuals were stratified into four risk groups via the conditional inference tree analysis. Compared to the low-risk group, all other groups had a significantly higher risk of developing functional limitation. The odds ratio comparing two extreme categories was 9.09 (95% confidence interval: 4.68, 17.6). Higher BMI, age, and comorbid disease were consistently identified as significant risk factors for functional decline among older individuals across all approaches and analyses. © 2017 The Obesity Society.

A new feedback image encryption scheme based on perturbation with dynamical compound chaotic sequence cipher generator

NASA Astrophysics Data System (ADS)

Tong, Xiaojun; Cui, Minggen; Wang, Zhu

2009-07-01

The design of the new compound two-dimensional chaotic function is presented by exploiting two one-dimensional chaotic functions which switch randomly, and the design is used as a chaotic sequence generator which is proved by Devaney's definition proof of chaos. The properties of compound chaotic functions are also proved rigorously. In order to improve the robustness against difference cryptanalysis and produce avalanche effect, a new feedback image encryption scheme is proposed using the new compound chaos by selecting one of the two one-dimensional chaotic functions randomly and a new image pixels method of permutation and substitution is designed in detail by array row and column random controlling based on the compound chaos. The results from entropy analysis, difference analysis, statistical analysis, sequence randomness analysis, cipher sensitivity analysis depending on key and plaintext have proven that the compound chaotic sequence cipher can resist cryptanalytic, statistical and brute-force attacks, and especially it accelerates encryption speed, and achieves higher level of security. By the dynamical compound chaos and perturbation technology, the paper solves the problem of computer low precision of one-dimensional chaotic function.

Individualized Positive Behavior Support in School Settings: A Meta-Analysis

ERIC Educational Resources Information Center

Goh, Ailsa E.; Bambara, Linda M.

2012-01-01

This meta-analysis examined school-based intervention research based on functional behavioral assessment (FBA) to determine the effectiveness of key individualized positive behavior support (IPBS) practices in school settings. In all, 83 studies representing 145 participants were included in the meta-analysis. Intervention, maintenance, and…

Clarifying Inconclusive Functional Analysis Results: Assessment and Treatment of Automatically Reinforced Aggression

PubMed Central

Saini, Valdeep; Greer, Brian D.; Fisher, Wayne W.

2016-01-01

We conducted a series of studies in which multiple strategies were used to clarify the inconclusive results of one boy’s functional analysis of aggression. Specifically, we (a) evaluated individual response topographies to determine the composition of aggregated response rates, (b) conducted a separate functional analysis of aggression after high rates of disruption masked the consequences maintaining aggression during the initial functional analysis, (c) modified the experimental design used during the functional analysis of aggression to improve discrimination and decrease interaction effects between conditions, and (d) evaluated a treatment matched to the reinforcer hypothesized to maintain aggression. An effective yet practical intervention for aggression was developed based on the results of these analyses and from data collected during the matched-treatment evaluation. PMID:25891269

Production of Printed Indexes of Chemical Reactions. I. Analysis of Functional Group Interconversions

ERIC Educational Resources Information Center

Clinging, R.; Lynch, M. F.

1973-01-01

A program is described which identifies functional group interconversion reactions, hydrogenations, and dehydrogenations in a data base containing structures encoded as Wiswesser Line Notations. Production of the data base is briefly described. (17 references) (Authors)

A Functional Analysis of Non-Vocal Verbal Behavior of a Young Child with Autism

ERIC Educational Resources Information Center

Normand, M. P.; Severtson, E. S.; Beavers, G. A.

2008-01-01

The functions of an American Sign Language response were experimentally evaluated with a young boy diagnosed with autism. A functional analysis procedure based on that reported by Lerman et al. (2005) was used to evaluate whether the target sign response would occur under mand, tact, mimetic, or control conditions. The target sign was observed…

Classroom-Based Functional Analysis and Intervention for Disruptive and Off-Task Behaviors

ERIC Educational Resources Information Center

Shumate, Emily D.; Wills, Howard P.

2010-01-01

Although there is a growing body of literature on the use of functional analysis in schools, there is a need for more demonstrations of this technology being used during the course of typical instruction. In this study, we conducted functional analyses of disruptive and off-task behavior in a reading classroom setting for 3 participants of typical…

INFANT SIGN TRAINING AND FUNCTIONAL ANALYSIS

PubMed Central

Normand, Matthew P; Machado, Mychal A; Hustyi, Kristin M; Morley, Allison J

2011-01-01

We taught manual signs to typically developing infants using a reversal design and caregiver-nominated stimuli. We delivered the stimuli on a time-based schedule during baseline. During the intervention, we used progressive prompting and reinforcement, described by Thompson et al. (2004, 2007), to establish mands. Following sign training, we conducted functional analyses and verified that the signs functioned as mands. These results provide preliminary validation for the verbal behavior functional analysis methodology and further evidence of the functional independence of verbal operants. PMID:21709786

A discrimlnant function approach to ecological site classification in northern New England

Treesearch

James M. Fincher; Marie-Louise Smith

1994-01-01

Describes one approach to ecologically based classification of upland forest community types of the White and Green Mountain physiographic regions. The classification approach is based on an intensive statistical analysis of the relationship between the communities and soil-site factors. Discriminant functions useful in distinguishing between types based on soil-site...

Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform

PubMed Central

Wang, Chun-Li; Yang, Yueh-Lung; Wu, Wen-Hsiang; Tsai, Tung-Hu; Chang, Hen-Hong

2016-01-01

We apply the recently developed adaptive non-harmonic model based on the wave-shape function, as well as the time-frequency analysis tool called synchrosqueezing transform (SST) to model and analyze oscillatory physiological signals. To demonstrate how the model and algorithm work, we apply them to study the pulse wave signal. By extracting features called the spectral pulse signature, and based on functional regression, we characterize the hemodynamics from the radial pulse wave signals recorded by the sphygmomanometer. Analysis results suggest the potential of the proposed signal processing approach to extract health-related hemodynamics features. PMID:27304979

Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform.

PubMed

Wu, Hau-Tieng; Wu, Han-Kuei; Wang, Chun-Li; Yang, Yueh-Lung; Wu, Wen-Hsiang; Tsai, Tung-Hu; Chang, Hen-Hong

2016-01-01

We apply the recently developed adaptive non-harmonic model based on the wave-shape function, as well as the time-frequency analysis tool called synchrosqueezing transform (SST) to model and analyze oscillatory physiological signals. To demonstrate how the model and algorithm work, we apply them to study the pulse wave signal. By extracting features called the spectral pulse signature, and based on functional regression, we characterize the hemodynamics from the radial pulse wave signals recorded by the sphygmomanometer. Analysis results suggest the potential of the proposed signal processing approach to extract health-related hemodynamics features.

SYNCSA--R tool for analysis of metacommunities based on functional traits and phylogeny of the community components.

PubMed

Debastiani, Vanderlei J; Pillar, Valério D

2012-08-01

SYNCSA is an R package for the analysis of metacommunities based on functional traits and phylogeny of the community components. It offers tools to calculate several matrix correlations that express trait-convergence assembly patterns, trait-divergence assembly patterns and phylogenetic signal in functional traits at the species pool level and at the metacommunity level. SYNCSA is a package for the R environment, under a GPL-2 open-source license and freely available on CRAN official web server for R (http://cran.r-project.org). vanderleidebastiani@yahoo.com.br.

[A functional analysis of healthcare auditors' skills in Venezuela, 2008].

PubMed

Chirinos-Muñoz, Mónica S

2010-10-01

Using functional analysis for identifying the basic, working, specific and generic skills and values which a health service auditor must have. Implementing the functional analysis technique with 10 experts, identifying specific, basic, generic skills and values by means of deductive logic. A functional map was obtained which started by establishing a key purpose based on improving healthcare and service quality from which three key functions emerged. The main functions and skills' units were then broken down into the competitive elements defining what a health service auditor is able to do. This functional map (following functional analysis methodology) shows in detail the simple and complex tasks which a healthcare auditor should apply in the workplace, adopting a forward management approach for improving healthcare and health service quality. This methodology, expressing logical-deductive awareness raising, provides expert consensual information validating each element regarding overall skills.

GOEAST: a web-based software toolkit for Gene Ontology enrichment analysis.

PubMed

Zheng, Qi; Wang, Xiu-Jie

2008-07-01

Gene Ontology (GO) analysis has become a commonly used approach for functional studies of large-scale genomic or transcriptomic data. Although there have been a lot of software with GO-related analysis functions, new tools are still needed to meet the requirements for data generated by newly developed technologies or for advanced analysis purpose. Here, we present a Gene Ontology Enrichment Analysis Software Toolkit (GOEAST), an easy-to-use web-based toolkit that identifies statistically overrepresented GO terms within given gene sets. Compared with available GO analysis tools, GOEAST has the following improved features: (i) GOEAST displays enriched GO terms in graphical format according to their relationships in the hierarchical tree of each GO category (biological process, molecular function and cellular component), therefore, provides better understanding of the correlations among enriched GO terms; (ii) GOEAST supports analysis for data from various sources (probe or probe set IDs of Affymetrix, Illumina, Agilent or customized microarrays, as well as different gene identifiers) and multiple species (about 60 prokaryote and eukaryote species); (iii) One unique feature of GOEAST is to allow cross comparison of the GO enrichment status of multiple experiments to identify functional correlations among them. GOEAST also provides rigorous statistical tests to enhance the reliability of analysis results. GOEAST is freely accessible at http://omicslab.genetics.ac.cn/GOEAST/

Comparative Network-Based Recovery Analysis and Proteomic Profiling of Neurological Changes in Valproic Acid-Treated Mice

PubMed Central

2013-01-01

Despite its prominence for characterization of complex mixtures, LC–MS/MS frequently fails to identify many proteins. Network-based analysis methods, based on protein–protein interaction networks (PPINs), biological pathways, and protein complexes, are useful for recovering non-detected proteins, thereby enhancing analytical resolution. However, network-based analysis methods do come in varied flavors for which the respective efficacies are largely unknown. We compare the recovery performance and functional insights from three distinct instances of PPIN-based approaches, viz., Proteomics Expansion Pipeline (PEP), Functional Class Scoring (FCS), and Maxlink, in a test scenario of valproic acid (VPA)-treated mice. We find that the most comprehensive functional insights, as well as best non-detected protein recovery performance, are derived from FCS utilizing real biological complexes. This outstrips other network-based methods such as Maxlink or Proteomics Expansion Pipeline (PEP). From FCS, we identified known biological complexes involved in epigenetic modifications, neuronal system development, and cytoskeletal rearrangements. This is congruent with the observed phenotype where adult mice showed an increase in dendritic branching to allow the rewiring of visual cortical circuitry and an improvement in their visual acuity when tested behaviorally. In addition, PEP also identified a novel complex, comprising YWHAB, NR1, NR2B, ACTB, and TJP1, which is functionally related to the observed phenotype. Although our results suggest different network analysis methods can produce different results, on the whole, the findings are mutually supportive. More critically, the non-overlapping information each provides can provide greater holistic understanding of complex phenotypes. PMID:23557376

Fuzzy cluster analysis of high-field functional MRI data.

PubMed

Windischberger, Christian; Barth, Markus; Lamm, Claus; Schroeder, Lee; Bauer, Herbert; Gur, Ruben C; Moser, Ewald

2003-11-01

Functional magnetic resonance imaging (fMRI) based on blood-oxygen level dependent (BOLD) contrast today is an established brain research method and quickly gains acceptance for complementary clinical diagnosis. However, neither the basic mechanisms like coupling between neuronal activation and haemodynamic response are known exactly, nor can the various artifacts be predicted or controlled. Thus, modeling functional signal changes is non-trivial and exploratory data analysis (EDA) may be rather useful. In particular, identification and separation of artifacts as well as quantification of expected, i.e. stimulus correlated, and novel information on brain activity is important for both, new insights in neuroscience and future developments in functional MRI of the human brain. After an introduction on fuzzy clustering and very high-field fMRI we present several examples where fuzzy cluster analysis (FCA) of fMRI time series helps to identify and locally separate various artifacts. We also present and discuss applications and limitations of fuzzy cluster analysis in very high-field functional MRI: differentiate temporal patterns in MRI using (a) a test object with static and dynamic parts, (b) artifacts due to gross head motion artifacts. Using a synthetic fMRI data set we quantitatively examine the influences of relevant FCA parameters on clustering results in terms of receiver-operator characteristics (ROC) and compare them with a commonly used model-based correlation analysis (CA) approach. The application of FCA in analyzing in vivo fMRI data is shown for (a) a motor paradigm, (b) data from multi-echo imaging, and (c) a fMRI study using mental rotation of three-dimensional cubes. We found that differentiation of true "neural" from false "vascular" activation is possible based on echo time dependence and specific activation levels, as well as based on their signal time-course. Exploratory data analysis methods in general and fuzzy cluster analysis in particular may help to identify artifacts and add novel and unexpected information valuable for interpretation, classification and characterization of functional MRI data which can be used to design new data acquisition schemes, stimulus presentations, neuro(physio)logical paradigms, as well as to improve quantitative biophysical models.

Function Allocation between Automation and Human Pilot for Airborne Separation Assurance

NASA Technical Reports Server (NTRS)

Idris, Husni; Enea, Gabriele; Lewis, TImothy A.

2016-01-01

Maintaining safe separation between aircraft is a key determinant of the airspace capacity to handle air transportation. With the advent of satellite-based surveillance, aircraft equipped with the needed technologies are now capable of maintaining awareness of their location in the airspace and sharing it with their surrounding traffic. As a result, concepts and cockpit automation are emerging to enable delegating the responsibility of maintaining safe separation from traffic to the pilot; thus increasing the airspace capacity by alleviating the limitation of the current non-scalable centralized ground-based system. In this paper, an analysis of allocating separation assurance functions to the human pilot and cockpit automation is presented to support the design of these concepts and technologies. A task analysis was conducted with the help of Petri nets to identify the main separation assurance functions and their interactions. Each function was characterized by three behavior levels that may be needed to perform the task: skill, rule and knowledge based levels. Then recommendations are made for allocating each function to an automation scale based on their behavior level characterization and with the help of Subject matter experts.

Efficient reliability analysis of structures with the rotational quasi-symmetric point- and the maximum entropy methods

NASA Astrophysics Data System (ADS)

Xu, Jun; Dang, Chao; Kong, Fan

2017-10-01

This paper presents a new method for efficient structural reliability analysis. In this method, a rotational quasi-symmetric point method (RQ-SPM) is proposed for evaluating the fractional moments of the performance function. Then, the derivation of the performance function's probability density function (PDF) is carried out based on the maximum entropy method in which constraints are specified in terms of fractional moments. In this regard, the probability of failure can be obtained by a simple integral over the performance function's PDF. Six examples, including a finite element-based reliability analysis and a dynamic system with strong nonlinearity, are used to illustrate the efficacy of the proposed method. All the computed results are compared with those by Monte Carlo simulation (MCS). It is found that the proposed method can provide very accurate results with low computational effort.

Quantum random oracle model for quantum digital signature

NASA Astrophysics Data System (ADS)

Shang, Tao; Lei, Qi; Liu, Jianwei

2016-10-01

The goal of this work is to provide a general security analysis tool, namely, the quantum random oracle (QRO), for facilitating the security analysis of quantum cryptographic protocols, especially protocols based on quantum one-way function. QRO is used to model quantum one-way function and different queries to QRO are used to model quantum attacks. A typical application of quantum one-way function is the quantum digital signature, whose progress has been hampered by the slow pace of the experimental realization. Alternatively, we use the QRO model to analyze the provable security of a quantum digital signature scheme and elaborate the analysis procedure. The QRO model differs from the prior quantum-accessible random oracle in that it can output quantum states as public keys and give responses to different queries. This tool can be a test bed for the cryptanalysis of more quantum cryptographic protocols based on the quantum one-way function.

PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

PubMed

Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Sederberg, Per B.; Hanson, Stephen José; Haxby, James V.; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine-learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability. PMID:19184561

Thalamic functional connectivity predicts seizure laterality in individual TLE patients: application of a biomarker development strategy.

PubMed

Barron, Daniel S; Fox, Peter T; Pardoe, Heath; Lancaster, Jack; Price, Larry R; Blackmon, Karen; Berry, Kristen; Cavazos, Jose E; Kuzniecky, Ruben; Devinsky, Orrin; Thesen, Thomas

2015-01-01

Noninvasive markers of brain function could yield biomarkers in many neurological disorders. Disease models constrained by coordinate-based meta-analysis are likely to increase this yield. Here, we evaluate a thalamic model of temporal lobe epilepsy that we proposed in a coordinate-based meta-analysis and extended in a diffusion tractography study of an independent patient population. Specifically, we evaluated whether thalamic functional connectivity (resting-state fMRI-BOLD) with temporal lobe areas can predict seizure onset laterality, as established with intracranial EEG. Twenty-four lesional and non-lesional temporal lobe epilepsy patients were studied. No significant differences in functional connection strength in patient and control groups were observed with Mann-Whitney Tests (corrected for multiple comparisons). Notwithstanding the lack of group differences, individual patient difference scores (from control mean connection strength) successfully predicted seizure onset zone as shown in ROC curves: discriminant analysis (two-dimensional) predicted seizure onset zone with 85% sensitivity and 91% specificity; logistic regression (four-dimensional) achieved 86% sensitivity and 100% specificity. The strongest markers in both analyses were left thalamo-hippocampal and right thalamo-entorhinal cortex functional connection strength. Thus, this study shows that thalamic functional connections are sensitive and specific markers of seizure onset laterality in individual temporal lobe epilepsy patients. This study also advances an overall strategy for the programmatic development of neuroimaging biomarkers in clinical and genetic populations: a disease model informed by coordinate-based meta-analysis was used to anatomically constrain individual patient analyses.

Computed tomographic-based quantification of emphysema and correlation to pulmonary function and mechanics.

PubMed

Washko, George R; Criner, Gerald J; Mohsenifar, Zab; Sciurba, Frank C; Sharafkhaneh, Amir; Make, Barry J; Hoffman, Eric A; Reilly, John J

2008-06-01

Computed tomographic based indices of emphysematous lung destruction may highlight differences in disease pathogenesis and further enable the classification of subjects with Chronic Obstructive Pulmonary Disease. While there are multiple techniques that can be utilized for such radiographic analysis, there is very little published information comparing the performance of these methods in a clinical case series. Our objective was to examine several quantitative and semi-quantitative methods for the assessment of the burden of emphysema apparent on computed tomographic scans and compare their ability to predict lung mechanics and function. Automated densitometric analysis was performed on 1094 computed tomographic scans collected upon enrollment into the National Emphysema Treatment Trial. Trained radiologists performed an additional visual grading of emphysema on high resolution CT scans. Full pulmonary function test results were available for correlation, with a subset of subjects having additional measurements of lung static recoil. There was a wide range of emphysematous lung destruction apparent on the CT scans and univariate correlations to measures of lung function were of modest strength. No single method of CT scan analysis clearly outperformed the rest of the group. Quantification of the burden of emphysematous lung destruction apparent on CT scan is a weak predictor of lung function and mechanics in severe COPD with no uniformly superior method found to perform this analysis. The CT based quantification of emphysema may augment pulmonary function testing in the characterization of COPD by providing complementary phenotypic information.

Functional diversity of fish in tropical estuaries: A traits-based approach of communities in Pernambuco, Brazil

NASA Astrophysics Data System (ADS)

Silva-Júnior, C. A. B.; Mérigot, B.; Lucena-Frédou, F.; Ferreira, B. P.; Coxey, M. S.; Rezende, S. M.; Frédou, T.

2017-11-01

Environmental changes and human activities may have strong impacts on biodiversity and ecosystem functioning. While biodiversity is traditionally based on species richness and composition, there is a growing concern to take into account functional diversity to assess and manage species communities. In spite of their economic importance, functional diversity quantified by a traits-based approach is still poorly documented in tropical estuaries. In this study, the functional diversity of fishes was investigated within four estuaries in Pernambuco state, northeast of Brazil. These areas are subject to different levels of human impact (e.g. mangrove deforestation, shrimp farming, fishing etc.) and environmental conditions. Fishes were collected during 34 scientific surveys. A total of 122 species were identified and 12 functional traits were quantified describing two main functions: food acquisition and locomotion. Fish abundance and functional dissimilarities data were combined into a multivariate analysis, the Double Principal Coordinate Analysis, to identify the functional typology of fish assemblages according to the estuary. Results showed that Itapissuma, the largest estuary with a wider mangrove forest area, differs from the other three estuaries, showing higher mean values per samples of species richness S and quadratic entropy Q. Similarly, it presented a different functional typology (the first two axes of the DPCoA account for 68.7% of total inertia, while those of a traditional PCA based solely on species abundances provided only 17.4%). Conversely, Suape, Sirinhaém, and to a lower extent Rio Formoso, showed more similarity in their diversity. This result was attributed to their predominantly marine influenced hydrological features, and similar levels of species abundances and in morphological traits. Overall, this study, combining diversity indices and a recent multivariate analysis to access species contribution to functional typology, allows to deepen diversity assessment by providing additional information regarding the functional pattern of fish assemblages.

Sparse dictionary learning for resting-state fMRI analysis

NASA Astrophysics Data System (ADS)

Lee, Kangjoo; Han, Paul Kyu; Ye, Jong Chul

2011-09-01

Recently, there has been increased interest in the usage of neuroimaging techniques to investigate what happens in the brain at rest. Functional imaging studies have revealed that the default-mode network activity is disrupted in Alzheimer's disease (AD). However, there is no consensus, as yet, on the choice of analysis method for the application of resting-state analysis for disease classification. This paper proposes a novel compressed sensing based resting-state fMRI analysis tool called Sparse-SPM. As the brain's functional systems has shown to have features of complex networks according to graph theoretical analysis, we apply a graph model to represent a sparse combination of information flows in complex network perspectives. In particular, a new concept of spatially adaptive design matrix has been proposed by implementing sparse dictionary learning based on sparsity. The proposed approach shows better performance compared to other conventional methods, such as independent component analysis (ICA) and seed-based approach, in classifying the AD patients from normal using resting-state analysis.

Intensity-based masking: A tool to improve functional connectivity results of resting-state fMRI.

PubMed

Peer, Michael; Abboud, Sami; Hertz, Uri; Amedi, Amir; Arzy, Shahar

2016-07-01

Seed-based functional connectivity (FC) of resting-state functional MRI data is a widely used methodology, enabling the identification of functional brain networks in health and disease. Based on signal correlations across the brain, FC measures are highly sensitive to noise. A somewhat neglected source of noise is the fMRI signal attenuation found in cortical regions in close vicinity to sinuses and air cavities, mainly in the orbitofrontal, anterior frontal and inferior temporal cortices. BOLD signal recorded at these regions suffers from dropout due to susceptibility artifacts, resulting in an attenuated signal with reduced signal-to-noise ratio in as many as 10% of cortical voxels. Nevertheless, signal attenuation is largely overlooked during FC analysis. Here we first demonstrate that signal attenuation can significantly influence FC measures by introducing false functional correlations and diminishing existing correlations between brain regions. We then propose a method for the detection and removal of the attenuated signal ("intensity-based masking") by fitting a Gaussian-based model to the signal intensity distribution and calculating an intensity threshold tailored per subject. Finally, we apply our method on real-world data, showing that it diminishes false correlations caused by signal dropout, and significantly improves the ability to detect functional networks in single subjects. Furthermore, we show that our method increases inter-subject similarity in FC, enabling reliable distinction of different functional networks. We propose to include the intensity-based masking method as a common practice in the pre-processing of seed-based functional connectivity analysis, and provide software tools for the computation of intensity-based masks on fMRI data. Hum Brain Mapp 37:2407-2418, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Acquisition of Picture Exchange-Based vs. Signed Mands and Implications to Teach Functional Communication Skills to Children with Autism

ERIC Educational Resources Information Center

Nam, Sang S.; Hwang, Young S.

2016-01-01

A literature review was conducted to describe important concepts involved in functional analysis of verbal behavior as well as to evaluate empirical research findings on acquisition of picture exchange-based vs. signed mands to suggest instructional implications for teachers and therapists to teach functional communication skills to children with…

Efficacy of a Systematic Process for Developing Function-Based Treatment for Young Children with Disabilities

ERIC Educational Resources Information Center

Aldosari, Mubarak S.

2016-01-01

This study conducted an in-depth analysis of the efficacy of the Decision Model in the development of function-based treatments for disruptive behaviors in four toddlers with disabilities aged from 26 to 34 months in inclusive toddler classrooms. The research was conducted in three parts. In Part 1, a functional behavioral assessment was conducted…

Decline of kidney function during the pre-dialysis period in chronic kidney disease patients: a systematic review and meta-analysis.

PubMed

Janmaat, Cynthia J; van Diepen, Merel; van Hagen, Cheyenne Ce; Rotmans, Joris I; Dekker, Friedo W; Dekkers, Olaf M

2018-01-01

Substantial heterogeneity exists in reported kidney function decline in pre-dialysis chronic kidney disease (CKD). By design, kidney function decline can be studied in CKD 3-5 cohorts or dialysis-based studies. In the latter, patients are selected based on the fact that they initiated dialysis, possibly leading to an overestimation of the true underlying kidney function decline in the pre-dialysis period. We performed a systematic review and meta-analysis to compare the kidney function decline during pre-dialysis in CKD stage 3-5 patients, in these two different study types. We searched PubMed, EMBASE, Web of Science and Cochrane to identify eligible studies reporting an estimated glomerular filtration rate (eGFR) decline (mL/min/1.73 m 2 ) in adult pre-dialysis CKD patients. Random-effects meta-analysis was performed to obtain weighted mean annual eGFR decline. We included 60 studies (43 CKD 3-5 cohorts and 17 dialysis-based studies). The meta-analysis yielded a weighted annual mean (95% CI) eGFR decline during pre-dialysis of 2.4 (95% CI: 2.2, 2.6) mL/min/1.73 m 2 in CKD 3-5 cohorts compared to 8.5 (95% CI: 6.8, 10.1) in dialysis-based studies (difference 6.0 [95% CI: 4.8, 7.2]). To conclude, dialysis-based studies report faster mean annual eGFR decline during pre-dialysis than CKD 3-5 cohorts. Thus, eGFR decline data from CKD 3-5 cohorts should be used to guide clinical decision making in CKD patients and for power calculations in randomized controlled trials with CKD progression during pre-dialysis as the outcome.

Modified Gaussian influence function of deformable mirror actuators.

PubMed

Huang, Linhai; Rao, Changhui; Jiang, Wenhan

2008-01-07

A new deformable mirror influence function based on a Gaussian function is introduced to analyze the fitting capability of a deformable mirror. The modified expressions for both azimuthal and radial directions are presented based on the analysis of the residual error between a measured influence function and a Gaussian influence function. With a simplex search method, we further compare the fitting capability of our proposed influence function to fit the data produced by a Zygo interferometer with that of a Gaussian influence function. The result indicates that the modified Gaussian influence function provides much better performance in data fitting.

Time-Varying Networks of Inter-Ictal Discharging Reveal Epileptogenic Zone.

PubMed

Zhang, Luyan; Liang, Yi; Li, Fali; Sun, Hongbin; Peng, Wenjing; Du, Peishan; Si, Yajing; Song, Limeng; Yu, Liang; Xu, Peng

2017-01-01

The neuronal synchronous discharging may cause an epileptic seizure. Currently, most of the studies conducted to investigate the mechanism of epilepsy are based on EEGs or functional magnetic resonance imaging (fMRI) recorded during the ictal discharging or the resting-state, and few studies have probed into the dynamic patterns during the inter-ictal discharging that are much easier to record in clinical applications. Here, we propose a time-varying network analysis based on adaptive directed transfer function to uncover the dynamic brain network patterns during the inter-ictal discharging. In addition, an algorithm based on the time-varying outflow of information derived from the network analysis is developed to detect the epileptogenic zone. The analysis performed revealed the time-varying network patterns during different stages of inter-ictal discharging; the epileptogenic zone was activated prior to the discharge onset then worked as the source to propagate the activity to other brain regions. Consistence between the epileptogenic zones detected by our proposed approach and the actual epileptogenic zones proved that time-varying network analysis could not only reveal the underlying neural mechanism of epilepsy, but also function as a useful tool in detecting the epileptogenic zone based on the EEGs in the inter-ictal discharging.

Assessing the equivalence of Web-based and paper-and-pencil questionnaires using differential item and test functioning (DIF and DTF) analysis: a case of the Four-Dimensional Symptom Questionnaire (4DSQ).

PubMed

Terluin, Berend; Brouwers, Evelien P M; Marchand, Miquelle A G; de Vet, Henrica C W

2018-05-01

Many paper-and-pencil (P&P) questionnaires have been migrated to electronic platforms. Differential item and test functioning (DIF and DTF) analysis constitutes a superior research design to assess measurement equivalence across modes of administration. The purpose of this study was to demonstrate an item response theory (IRT)-based DIF and DTF analysis to assess the measurement equivalence of a Web-based version and the original P&P format of the Four-Dimensional Symptom Questionnaire (4DSQ), measuring distress, depression, anxiety, and somatization. The P&P group (n = 2031) and the Web group (n = 958) consisted of primary care psychology clients. Unidimensionality and local independence of the 4DSQ scales were examined using IRT and Yen's Q3. Bifactor modeling was used to assess the scales' essential unidimensionality. Measurement equivalence was assessed using IRT-based DIF analysis using a 3-stage approach: linking on the latent mean and variance, selection of anchor items, and DIF testing using the Wald test. DTF was evaluated by comparing expected scale scores as a function of the latent trait. The 4DSQ scales proved to be essentially unidimensional in both modalities. Five items, belonging to the distress and somatization scales, displayed small amounts of DIF. DTF analysis revealed that the impact of DIF on the scale level was negligible. IRT-based DIF and DTF analysis is demonstrated as a way to assess the equivalence of Web-based and P&P questionnaire modalities. Data obtained with the Web-based 4DSQ are equivalent to data obtained with the P&P version.

Web-Based OPACs in Indian Academic Libraries: A Functional Comparison

ERIC Educational Resources Information Center

Kapoor, Kanta; Goyal, O. P.

2007-01-01

Purpose: The paper seeks to provide a comparative analysis of the functionality of five web-based OPACs available in Indian academic libraries. Design/methodology/approach: Same-topic searches were carried out by three researchers on the web-based OPACs of Libsys, VTLS's iPortal, NewGenLib, Troodon, and Alice for Windows, implemented in five…

Fuzzy parametric uncertainty analysis of linear dynamical systems: A surrogate modeling approach

NASA Astrophysics Data System (ADS)

Chowdhury, R.; Adhikari, S.

2012-10-01

Uncertainty propagation engineering systems possess significant computational challenges. This paper explores the possibility of using correlated function expansion based metamodelling approach when uncertain system parameters are modeled using Fuzzy variables. In particular, the application of High-Dimensional Model Representation (HDMR) is proposed for fuzzy finite element analysis of dynamical systems. The HDMR expansion is a set of quantitative model assessment and analysis tools for capturing high-dimensional input-output system behavior based on a hierarchy of functions of increasing dimensions. The input variables may be either finite-dimensional (i.e., a vector of parameters chosen from the Euclidean space RM) or may be infinite-dimensional as in the function space CM[0,1]. The computational effort to determine the expansion functions using the alpha cut method scales polynomially with the number of variables rather than exponentially. This logic is based on the fundamental assumption underlying the HDMR representation that only low-order correlations among the input variables are likely to have significant impacts upon the outputs for most high-dimensional complex systems. The proposed method is integrated with a commercial Finite Element software. Modal analysis of a simplified aircraft wing with Fuzzy parameters has been used to illustrate the generality of the proposed approach. In the numerical examples, triangular membership functions have been used and the results have been validated against direct Monte Carlo simulations.

Variability in the Results of Meta-Analysis as a Function of Comparing Effect Sizes Based on Scores from Noncomparable Measures: A Simulation Study

ERIC Educational Resources Information Center

Nugent, William R.

2017-01-01

Meta-analysis is a significant methodological advance that is increasingly important in research synthesis. Fundamental to meta-analysis is the presumption that effect sizes, such as the standardized mean difference (SMD), based on scores from different measures are comparable. It has been argued that population observed score SMDs based on scores…

Development of Activity-based Cost Functions for Cellulase, Invertase, and Other Enzymes

NASA Astrophysics Data System (ADS)

Stowers, Chris C.; Ferguson, Elizabeth M.; Tanner, Robert D.

As enzyme chemistry plays an increasingly important role in the chemical industry, cost analysis of these enzymes becomes a necessity. In this paper, we examine the aspects that affect the cost of enzymes based upon enzyme activity. The basis for this study stems from a previously developed objective function that quantifies the tradeoffs in enzyme purification via the foam fractionation process (Cherry et al., Braz J Chem Eng 17:233-238, 2000). A generalized cost function is developed from our results that could be used to aid in both industrial and lab scale chemical processing. The generalized cost function shows several nonobvious results that could lead to significant savings. Additionally, the parameters involved in the operation and scaling up of enzyme processing could be optimized to minimize costs. We show that there are typically three regimes in the enzyme cost analysis function: the low activity prelinear region, the moderate activity linear region, and high activity power-law region. The overall form of the cost analysis function appears to robustly fit the power law form.

An arc tangent function demodulation method of fiber-optic Fabry-Perot high-temperature pressure sensor

NASA Astrophysics Data System (ADS)

Ren, Qianyu; Li, Junhong; Hong, Yingping; Jia, Pinggang; Xiong, Jijun

2017-09-01

A new demodulation algorithm of the fiber-optic Fabry-Perot cavity length based on the phase generated carrier (PGC) is proposed in this paper, which can be applied in the high-temperature pressure sensor. This new algorithm based on arc tangent function outputs two orthogonal signals by utilizing an optical system, which is designed based on the field-programmable gate array (FPGA) to overcome the range limit of the original PGC arc tangent function demodulation algorithm. The simulation and analysis are also carried on. According to the analysis of demodulation speed and precision, the simulation of different numbers of sampling points, and measurement results of the pressure sensor, the arc tangent function demodulation method has good demodulation results: 1 MHz processing speed of single data and less than 1% error showing practical feasibility in the fiber-optic Fabry-Perot cavity length demodulation of the Fabry-Perot high-temperature pressure sensor.

An Energy-Based Limit State Function for Estimation of Structural Reliability in Shock Environments

DOE PAGES

Guthrie, Michael A.

2013-01-01

limit state function is developed for the estimation of structural reliability in shock environments. This limit state function uses peak modal strain energies to characterize environmental severity and modal strain energies at failure to characterize the structural capacity. The Hasofer-Lind reliability index is briefly reviewed and its computation for the energy-based limit state function is discussed. Applications to two degree of freedom mass-spring systems and to a simple finite element model are considered. For these examples, computation of the reliability index requires little effort beyond a modal analysis, but still accounts for relevant uncertainties in both the structure and environment.more » For both examples, the reliability index is observed to agree well with the results of Monte Carlo analysis. In situations where fast, qualitative comparison of several candidate designs is required, the reliability index based on the proposed limit state function provides an attractive metric which can be used to compare and control reliability.« less

Validation of Skeletal Muscle cis-Regulatory Module Predictions Reveals Nucleotide Composition Bias in Functional Enhancers

PubMed Central

Kwon, Andrew T.; Chou, Alice Yi; Arenillas, David J.; Wasserman, Wyeth W.

2011-01-01

We performed a genome-wide scan for muscle-specific cis-regulatory modules (CRMs) using three computational prediction programs. Based on the predictions, 339 candidate CRMs were tested in cell culture with NIH3T3 fibroblasts and C2C12 myoblasts for capacity to direct selective reporter gene expression to differentiated C2C12 myotubes. A subset of 19 CRMs validated as functional in the assay. The rate of predictive success reveals striking limitations of computational regulatory sequence analysis methods for CRM discovery. Motif-based methods performed no better than predictions based only on sequence conservation. Analysis of the properties of the functional sequences relative to inactive sequences identifies nucleotide sequence composition can be an important characteristic to incorporate in future methods for improved predictive specificity. Muscle-related TFBSs predicted within the functional sequences display greater sequence conservation than non-TFBS flanking regions. Comparison with recent MyoD and histone modification ChIP-Seq data supports the validity of the functional regions. PMID:22144875

USE OF WEIBULL FUNCTION FOR NON-LINEAR ANALYSIS OF EFFECTS OF LOW LEVELS OF SIMULATED HERBICIDE DRIFT ON PLANTS

EPA Science Inventory

We compared two regression models, which are based on the Weibull and probit functions, for the analysis of pesticide toxicity data from laboratory studies on Illinois crop and native plant species. Both mathematical models are continuous, differentiable, strictly positive, and...

Atlas-Based Ventricular Shape Analysis for Understanding Congenital Heart Disease.

PubMed

Farrar, Genevieve; Suinesiaputra, Avan; Gilbert, Kathleen; Perry, James C; Hegde, Sanjeet; Marsden, Alison; Young, Alistair A; Omens, Jeffrey H; McCulloch, Andrew D

2016-12-01

Congenital heart disease is associated with abnormal ventricular shape that can affect wall mechanics and may be predictive of long-term adverse outcomes. Atlas-based parametric shape analysis was used to analyze ventricular geometries of eight adolescent or adult single-ventricle CHD patients with tricuspid atresia and Fontans. These patients were compared with an "atlas" of non-congenital asymptomatic volunteers, resulting in a set of z-scores which quantify deviations from the control population distribution on a patient-by-patient basis. We examined the potential of these scores to: (1) quantify abnormalities of ventricular geometry in single ventricle physiologies relative to the normal population; (2) comprehensively quantify wall motion in CHD patients; and (3) identify possible relationships between ventricular shape and wall motion that may reflect underlying functional defects or remodeling in CHD patients. CHD ventricular geometries at end-diastole and end-systole were individually compared with statistical shape properties of an asymptomatic population from the Cardiac Atlas Project. Shape analysis-derived model properties, and myocardial wall motions between end-diastole and end-systole, were compared with physician observations of clinical functional parameters. Relationships between altered shape and altered function were evaluated via correlations between atlas-based shape and wall motion scores. Atlas-based shape analysis identified a diverse set of specific quantifiable abnormalities in ventricular geometry or myocardial wall motion in all subjects. Moreover, this initial cohort displayed significant relationships between specific shape abnormalities such as increased ventricular sphericity and functional defects in myocardial deformation, such as decreased long-axis wall motion. These findings suggest that atlas-based ventricular shape analysis may be a useful new tool in the management of patients with CHD who are at risk of impaired ventricular wall mechanics and chamber remodeling.

ACCEPTANCE OF FUNCTIONAL FOOD AMONG CHILEAN CONSUMERS: APPLE LEATHER.

PubMed

van Vliet, Maya; Adasme-Berrios, Cristian; Schnettler, Berta

2015-10-01

the aim of this study is to measure acceptance of a specific functional food: apple (fruit) leather, based on organoleptic characteristics and to identify consumer types and preferences for natural additives which increase the product's functionality and meet current nutritional needs. a sample of 800 consumers provided an evaluation of apple leather in terms of acceptance (liking). A sensorial panel was carried out using a 9-point hedonic scale. Cluster analysis was used to identify different acceptance-based consumer types. In addition, a conjoint analysis was carried out to determine preference for different additives. the cluster analysis resulted in four groups with significant differences in the average likings obtained from the sensory panel. Results indicate that the sweetness of the tested apple leather was evaluated best among all groups and, on average, color was rated as the worst attribute. However, overall likings differ significantly between groups. Results from the conjoint analysis indicate that, in general, consumers prefer natural additives included in the product which enhance functionality. although there is a "global acceptance" of the product, there are significant differences between groups. The results of the conjoint analysis indicate that, in general, consumers prefer the aggregation of natural additives which increase the product's functionality. Apple leather with natural additives, such as anticariogenics and antioxidants, can be considered a functional substitute of unhealthy snacks and/or sweets. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

Silica functionalized Cu(II) acetylacetonate Schiff base complex: An efficient catalyst for the oxidative condensation reaction of benzyl alcohol with amines

NASA Astrophysics Data System (ADS)

Anbarasu, G.; Malathy, M.; Karthikeyan, P.; Rajavel, R.

2017-09-01

Silica functionalized Cu(II) acetylacetonate Schiff base complex via the one pot reaction of silica functionalized 3-aminopropyltriethoxysilane with acetyl acetone and copper acetate has been reported. The synthesized material was well characterized by analytical techniques such as FT-IR, UV-DRS, XRD, SEM-EDX, HR-TEM, EPR, ICP-AES and BET analysis. The characterization results confirmed the grafting of Cu(II) Schiff base complex on the silica surface. The catalytic activity of synthesized silica functionalized Cu(II) acetylacetonate Schiff base complex was evaluated through the oxidative condensation reaction of benzyl alcohol to imine.

Skeletal effects in Angle Class II/1 patients treated with the functional regulator type II : Cephalometric and tensor analysis.

PubMed

Schulz, Simone; Koos, Bernd; Duske, Kathrin; Stahl, Franka

2016-11-01

The purpose of this work was to employ both cephalometric and tensor analysis in characterizing the skeletal changes experienced by patients with Angle Class II/1 malocclusion during functional orthodontic treatment with the functional regulator type II. A total of 23 patients with Class II/1 malocclusion based on lateral cephalograms obtained before and after treatment with the functional regulator type II were analyzed. Another 23 patients with Angle Class II/1 malocclusion who had not undergone treatment were included as controls. Our cephalometric data attest to significant therapeutic effects of the functional regulator type II on the skeletal mandibular system, including significant advancement of the mandible, increases in effective mandibular length with enhancement of the chin profile, and reduction of growth-related bite deepening. No treatment-related effects were observed at the cranial-base and midface levels. In addition, tensor analysis revealed significant stimulation of mandibular growth in sagittal directions, without indications of growth effects on the maxilla. Its growth-pattern findings differed from those of cephalometric analysis by indicating that the appliance did promote horizontal development, which supports the functional orthodontic treatment effect in Angle Class II/1 cases. Tensor analysis yielded additional insights into sagittal and vertical growth changes not identifiable by strictly cephalometric means. The functional regulator type II was an effective treatment modality for Angle Class II/1 malocclusion and influenced the skeletal development of these patients in favorable ways.

Poly(styrene-co-N-methacryloyl-l-phenylalanine methyl ester)-functionalized magnetic nanoparticles as sorbents for the analysis of sodium benzoate in beverages.

PubMed

Ji, Shilei; Li, Nan; Qi, Li; Wang, Minglin

2017-01-01

In this study, poly(styrene-co-N-methacryloyl-l-phenylalanine methyl ester)-functionalized magnetic nanoparticles were constructed and used as magnetic solid-phase extraction sorbents for analysis of food preservatives in beverages. To prepare the poly(amino acid)-based sorbents, N-methacryloyl-l-phenylalanine methyl ester, and styrene served as the functional monomers and modified onto the magnetic nanoparticles via free radical polymerization. Interestingly, compared with propylparaben and potassium sorbate, the proposed poly(amino acid)-based sorbents showed a good selectivity to sodium benzoate. The adsorption capacity of the sorbents to sodium benzoate was 6.08 ± 0.31 mg/g. Moreover, the fast adsorption equilibrium could be reached within 5 min. Further, the resultant poly(amino acid)-based sorbents were applied in the analysis of sodium benzoate in real beverage samples. The results proved that the proposed magnetic solid-phase extraction sorbents have a great potential for the analysis of preservatives in food samples. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Towards fully automated structure-based function prediction in structural genomics: a case study.

PubMed

Watson, James D; Sanderson, Steve; Ezersky, Alexandra; Savchenko, Alexei; Edwards, Aled; Orengo, Christine; Joachimiak, Andrzej; Laskowski, Roman A; Thornton, Janet M

2007-04-13

As the global Structural Genomics projects have picked up pace, the number of structures annotated in the Protein Data Bank as hypothetical protein or unknown function has grown significantly. A major challenge now involves the development of computational methods to assign functions to these proteins accurately and automatically. As part of the Midwest Center for Structural Genomics (MCSG) we have developed a fully automated functional analysis server, ProFunc, which performs a battery of analyses on a submitted structure. The analyses combine a number of sequence-based and structure-based methods to identify functional clues. After the first stage of the Protein Structure Initiative (PSI), we review the success of the pipeline and the importance of structure-based function prediction. As a dataset, we have chosen all structures solved by the MCSG during the 5 years of the first PSI. Our analysis suggests that two of the structure-based methods are particularly successful and provide examples of local similarity that is difficult to identify using current sequence-based methods. No one method is successful in all cases, so, through the use of a number of complementary sequence and structural approaches, the ProFunc server increases the chances that at least one method will find a significant hit that can help elucidate function. Manual assessment of the results is a time-consuming process and subject to individual interpretation and human error. We present a method based on the Gene Ontology (GO) schema using GO-slims that can allow the automated assessment of hits with a success rate approaching that of expert manual assessment.

A novel analysis method for near infrared spectroscopy based on Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Zhou, Zhenyu; Yang, Hongyu; Liu, Yun; Ruan, Zongcai; Luo, Qingming; Gong, Hui; Lu, Zuhong

2007-05-01

Near Infrared Imager (NIRI) has been widely used to access the brain functional activity non-invasively. We use a portable, multi-channel and continuous-wave NIR topography instrument to measure the concentration changes of each hemoglobin species and map cerebral cortex functional activation. By extracting some essential features from the BOLD signals, optical tomography is able to be a new way of neuropsychological studies. Fourier spectral analysis provides a common framework for examining the distribution of global energy in the frequency domain. However, this method assumes that the signal should be stationary, which limits its application in non-stationary system. The hemoglobin species concentration changes are of such kind. In this work we develop a new signal processing method using Hilbert-Huang transform to perform spectral analysis of the functional NIRI signals. Compared with wavelet based multi-resolution analysis (MRA), we demonstrated the extraction of task related signal for observation of activation in the prefrontal cortex (PFC) in vision stimulation experiment. This method provides a new analysis tool for functional NIRI signals. Our experimental results show that the proposed approach provides the unique method for reconstructing target signal without losing original information and enables us to understand the episode of functional NIRI more precisely.

Divergence of Structure and Function in the Haloacid Dehalogenase Enzyme Superfamily: Bacteroides thetaiotaomicron BT2127 is an Inorganic Pyrophosphatase+

PubMed Central

Huang, Hua; Yury, Patskovsky; Toro, Rafael; Farelli, Jeremiah D.; Pandya, Chetanya; Almo, Steven C.; Allen, Karen N.; Dunaway-Mariano, Debra

2012-01-01

The explosion of protein sequence information requires that current strategies for function assignment must evolve to complement experimental approaches with computationally-based function prediction. This necessitates the development of strategies based on the identification of sequence markers in the form of specificity determinants and a more informed definition of orthologues. Herein, we have undertaken the function assignment of the unknown Haloalkanoate Dehalogenase superfamily member BT2127 (Uniprot accession # Q8A5V9) from Bacteroides thetaiotaomicron using an integrated bioinformatics/structure/mechanism approach. The substrate specificity profile and steady-state rate constants of BT2127 (with kcat/Km value for pyrophosphate of ∼1 × 105 M−1 s−1), together with the gene context, supports the assigned in vivo function as an inorganic pyrophosphatase. The X-ray structural analysis of the wild-type BT2127 and several variants generated by site-directed mutagenesis shows that substrate discrimination is based, in part, on active site space restrictions imposed by the cap domain (specifically by residues Tyr76 and Glu47). Structure guided site directed mutagenesis coupled with kinetic analysis of the mutant enzymes identified the residues required for catalysis, substrate binding, and domain-domain association. Based on this structure-function analysis, the catalytic residues Asp11, Asp13, Thr113, and Lys147 as well the metal binding residues Asp171, Asn172 and Glu47 were used as markers to confirm BT2127 orthologues identified via sequence searches. This bioinformatic analysis demonstrated that the biological range of BT2127 orthologue is restricted to the phylum Bacteroidetes/Chlorobi. The key structural determinants in the divergence of BT2127 and its closest homologue β-phosphoglucomutase control the leaving group size (phosphate vs. glucose-phosphate) and the position of the Asp acid/base in the open vs. closed conformations. HADSF pyrophosphatases represent a third mechanistic and fold type for bacterial pyrophosphatases. PMID:21894910

Security Analysis of Selected AMI Failure Scenarios Using Agent Based Game Theoretic Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abercrombie, Robert K; Schlicher, Bob G; Sheldon, Frederick T

Information security analysis can be performed using game theory implemented in dynamic Agent Based Game Theoretic (ABGT) simulations. Such simulations can be verified with the results from game theory analysis and further used to explore larger scale, real world scenarios involving multiple attackers, defenders, and information assets. We concentrated our analysis on the Advanced Metering Infrastructure (AMI) functional domain which the National Electric Sector Cyber security Organization Resource (NESCOR) working group has currently documented 29 failure scenarios. The strategy for the game was developed by analyzing five electric sector representative failure scenarios contained in the AMI functional domain. From thesemore » five selected scenarios, we characterize them into three specific threat categories affecting confidentiality, integrity and availability (CIA). The analysis using our ABGT simulation demonstrates how to model the AMI functional domain using a set of rationalized game theoretic rules decomposed from the failure scenarios in terms of how those scenarios might impact the AMI network with respect to CIA.« less

The Researches on Damage Detection Method for Truss Structures

NASA Astrophysics Data System (ADS)

Wang, Meng Hong; Cao, Xiao Nan

2018-06-01

This paper presents an effective method to detect damage in truss structures. Numerical simulation and experimental analysis were carried out on a damaged truss structure under instantaneous excitation. The ideal excitation point and appropriate hammering method were determined to extract time domain signals under two working conditions. The frequency response function and principal component analysis were used for data processing, and the angle between the frequency response function vectors was selected as a damage index to ascertain the location of a damaged bar in the truss structure. In the numerical simulation, the time domain signal of all nodes was extracted to determine the location of the damaged bar. In the experimental analysis, the time domain signal of a portion of the nodes was extracted on the basis of an optimal sensor placement method based on the node strain energy coefficient. The results of the numerical simulation and experimental analysis showed that the damage detection method based on the frequency response function and principal component analysis could locate the damaged bar accurately.

First passage time: Connecting random walks to functional responses in heterogeneous environments (Invited)

NASA Astrophysics Data System (ADS)

Lewis, M. A.; McKenzie, H.; Merrill, E.

2010-12-01

In this talk I will outline first passage time analysis for animals undertaking complex movement patterns, and will demonstrate how first passage time can be used to derive functional responses in predator prey systems. The result is a new approach to understanding type III functional responses based on a random walk model. I will extend the analysis to heterogeneous environments to assess the effects of linear features on functional responses in wolves and elk using GPS tracking data.

Bayesian hierarchical functional data analysis via contaminated informative priors.

PubMed

Scarpa, Bruno; Dunson, David B

2009-09-01

A variety of flexible approaches have been proposed for functional data analysis, allowing both the mean curve and the distribution about the mean to be unknown. Such methods are most useful when there is limited prior information. Motivated by applications to modeling of temperature curves in the menstrual cycle, this article proposes a flexible approach for incorporating prior information in semiparametric Bayesian analyses of hierarchical functional data. The proposed approach is based on specifying the distribution of functions as a mixture of a parametric hierarchical model and a nonparametric contamination. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process. In the motivating application, the contamination component allows unanticipated curve shapes in unhealthy menstrual cycles. Methods are developed for posterior computation, and the approach is applied to data from a European fecundability study.

Idiopathic Pulmonary Fibrosis: Data-driven Textural Analysis of Extent of Fibrosis at Baseline and 15-Month Follow-up.

PubMed

Humphries, Stephen M; Yagihashi, Kunihiro; Huckleberry, Jason; Rho, Byung-Hak; Schroeder, Joyce D; Strand, Matthew; Schwarz, Marvin I; Flaherty, Kevin R; Kazerooni, Ella A; van Beek, Edwin J R; Lynch, David A

2017-10-01

Purpose To evaluate associations between pulmonary function and both quantitative analysis and visual assessment of thin-section computed tomography (CT) images at baseline and at 15-month follow-up in subjects with idiopathic pulmonary fibrosis (IPF). Materials and Methods This retrospective analysis of preexisting anonymized data, collected prospectively between 2007 and 2013 in a HIPAA-compliant study, was exempt from additional institutional review board approval. The extent of lung fibrosis at baseline inspiratory chest CT in 280 subjects enrolled in the IPF Network was evaluated. Visual analysis was performed by using a semiquantitative scoring system. Computer-based quantitative analysis included CT histogram-based measurements and a data-driven textural analysis (DTA). Follow-up CT images in 72 of these subjects were also analyzed. Univariate comparisons were performed by using Spearman rank correlation. Multivariate and longitudinal analyses were performed by using a linear mixed model approach, in which models were compared by using asymptotic χ 2 tests. Results At baseline, all CT-derived measures showed moderate significant correlation (P < .001) with pulmonary function. At follow-up CT, changes in DTA scores showed significant correlation with changes in both forced vital capacity percentage predicted (ρ = -0.41, P < .001) and diffusing capacity for carbon monoxide percentage predicted (ρ = -0.40, P < .001). Asymptotic χ 2 tests showed that inclusion of DTA score significantly improved fit of both baseline and longitudinal linear mixed models in the prediction of pulmonary function (P < .001 for both). Conclusion When compared with semiquantitative visual assessment and CT histogram-based measurements, DTA score provides additional information that can be used to predict diminished function. Automatic quantification of lung fibrosis at CT yields an index of severity that correlates with visual assessment and functional change in subjects with IPF. © RSNA, 2017.

A non-stationary cost-benefit based bivariate extreme flood estimation approach

NASA Astrophysics Data System (ADS)

Qi, Wei; Liu, Junguo

2018-02-01

Cost-benefit analysis and flood frequency analysis have been integrated into a comprehensive framework to estimate cost effective design values. However, previous cost-benefit based extreme flood estimation is based on stationary assumptions and analyze dependent flood variables separately. A Non-Stationary Cost-Benefit based bivariate design flood estimation (NSCOBE) approach is developed in this study to investigate influence of non-stationarities in both the dependence of flood variables and the marginal distributions on extreme flood estimation. The dependence is modeled utilizing copula functions. Previous design flood selection criteria are not suitable for NSCOBE since they ignore time changing dependence of flood variables. Therefore, a risk calculation approach is proposed based on non-stationarities in both marginal probability distributions and copula functions. A case study with 54-year observed data is utilized to illustrate the application of NSCOBE. Results show NSCOBE can effectively integrate non-stationarities in both copula functions and marginal distributions into cost-benefit based design flood estimation. It is also found that there is a trade-off between maximum probability of exceedance calculated from copula functions and marginal distributions. This study for the first time provides a new approach towards a better understanding of influence of non-stationarities in both copula functions and marginal distributions on extreme flood estimation, and could be beneficial to cost-benefit based non-stationary bivariate design flood estimation across the world.

Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster.

PubMed

Wan, Cen; Lees, Jonathan G; Minneci, Federico; Orengo, Christine A; Jones, David T

2017-10-01

Accurate gene or protein function prediction is a key challenge in the post-genome era. Most current methods perform well on molecular function prediction, but struggle to provide useful annotations relating to biological process functions due to the limited power of sequence-based features in that functional domain. In this work, we systematically evaluate the predictive power of temporal transcription expression profiles for protein function prediction in Drosophila melanogaster. Our results show significantly better performance on predicting protein function when transcription expression profile-based features are integrated with sequence-derived features, compared with the sequence-derived features alone. We also observe that the combination of expression-based and sequence-based features leads to further improvement of accuracy on predicting all three domains of gene function. Based on the optimal feature combinations, we then propose a novel multi-classifier-based function prediction method for Drosophila melanogaster proteins, FFPred-fly+. Interpreting our machine learning models also allows us to identify some of the underlying links between biological processes and developmental stages of Drosophila melanogaster.

Segmentation-based wavelet transform for still-image compression

NASA Astrophysics Data System (ADS)

Mozelle, Gerard; Seghier, Abdellatif; Preteux, Francoise J.

1996-10-01

In order to address simultaneously the two functionalities, content-based scalability required by MPEG-4, we introduce a segmentation-based wavelet transform (SBWT). SBWT takes into account both the mathematical properties of multiresolution analysis and the flexibility of region-based approaches for image compression. The associated methodology has two stages: 1) image segmentation into convex and polygonal regions; 2) 2D-wavelet transform of the signal corresponding to each region. In this paper, we have mathematically studied a method for constructing a multiresolution analysis (VjOmega)j (epsilon) N adapted to a polygonal region which provides an adaptive region-based filtering. The explicit construction of scaling functions, pre-wavelets and orthonormal wavelets bases defined on a polygon is carried out by using scaling functions is established by using the theory of Toeplitz operators. The corresponding expression can be interpreted as a location property which allow defining interior and boundary scaling functions. Concerning orthonormal wavelets and pre-wavelets, a similar expansion is obtained by taking advantage of the properties of the orthogonal projector P(V(j(Omega )) perpendicular from the space Vj(Omega ) + 1 onto the space (Vj(Omega )) perpendicular. Finally the mathematical results provide a simple and fast algorithm adapted to polygonal regions.

Virtual-Reality-Based Social Interaction Training for Children with High-Functioning Autism

ERIC Educational Resources Information Center

Ke, Fengfeng; Im, Tami

2013-01-01

Employing the multiple-baseline across-subjects design, the authors examined the implementation and potential effect of a virtual-reality-based social interaction program on the interaction and communication performance of children with high functioning autism. The data were collected via behavior observation and analysis, questionnaires, and…

Resting-state functional brain connectivity: lessons from functional near-infrared spectroscopy.

PubMed

Niu, Haijing; He, Yong

2014-04-01

Resting-state functional near-infrared spectroscopy (R-fNIRS) is an active area of interest and is currently attracting considerable attention as a new imaging tool for the study of resting-state brain function. Using variations in hemodynamic concentration signals, R-fNIRS measures the brain's low-frequency spontaneous neural activity, combining the advantages of portability, low-cost, high temporal sampling rate and less physical burden to participants. The temporal synchronization of spontaneous neuronal activity in anatomically separated regions is referred to as resting-state functional connectivity (RSFC). In the past several years, an increasing body of R-fNIRS RSFC studies has led to many important findings about functional integration among local or whole-brain regions by measuring inter-regional temporal synchronization. Here, we summarize recent advances made in the R-fNIRS RSFC methodologies, from the detection of RSFC (e.g., seed-based correlation analysis, independent component analysis, whole-brain correlation analysis, and graph-theoretical topological analysis), to the assessment of RSFC performance (e.g., reliability, repeatability, and validity), to the application of RSFC in studying normal development and brain disorders. The literature reviewed here suggests that RSFC analyses based on R-fNIRS data are valid and reliable for the study of brain function in healthy and diseased populations, thus providing a promising imaging tool for cognitive science and clinics.

Wavelet-based clustering of resting state MRI data in the rat.

PubMed

Medda, Alessio; Hoffmann, Lukas; Magnuson, Matthew; Thompson, Garth; Pan, Wen-Ju; Keilholz, Shella

2016-01-01

While functional connectivity has typically been calculated over the entire length of the scan (5-10min), interest has been growing in dynamic analysis methods that can detect changes in connectivity on the order of cognitive processes (seconds). Previous work with sliding window correlation has shown that changes in functional connectivity can be observed on these time scales in the awake human and in anesthetized animals. This exciting advance creates a need for improved approaches to characterize dynamic functional networks in the brain. Previous studies were performed using sliding window analysis on regions of interest defined based on anatomy or obtained from traditional steady-state analysis methods. The parcellation of the brain may therefore be suboptimal, and the characteristics of the time-varying connectivity between regions are dependent upon the length of the sliding window chosen. This manuscript describes an algorithm based on wavelet decomposition that allows data-driven clustering of voxels into functional regions based on temporal and spectral properties. Previous work has shown that different networks have characteristic frequency fingerprints, and the use of wavelets ensures that both the frequency and the timing of the BOLD fluctuations are considered during the clustering process. The method was applied to resting state data acquired from anesthetized rats, and the resulting clusters agreed well with known anatomical areas. Clusters were highly reproducible across subjects. Wavelet cross-correlation values between clusters from a single scan were significantly higher than the values from randomly matched clusters that shared no temporal information, indicating that wavelet-based analysis is sensitive to the relationship between areas. Copyright © 2015 Elsevier Inc. All rights reserved.

First Monte Carlo analysis of fragmentation functions from single-inclusive e + e - annihilation

DOE PAGES

Sato, Nobuo; Ethier, J. J.; Melnitchouk, W.; ...

2016-12-02

Here, we perform the first iterative Monte Carlo (IMC) analysis of fragmentation functions constrained by all available data from single-inclusive $e^+ e^-$ annihilation into pions and kaons. The IMC method eliminates potential bias in traditional analyses based on single fits introduced by fixing parameters not well contrained by the data, and provides a statistically rigorous determination of uncertainties. Our analysis reveals specific features of fragmentation functions using the new IMC methodology and those obtained from previous analyses, especially for light quarks and for strange quark fragmentation to kaons.

[Design and implementation of online statistical analysis function in information system of air pollution and health impact monitoring].

PubMed

Lü, Yiran; Hao, Shuxin; Zhang, Guoqing; Liu, Jie; Liu, Yue; Xu, Dongqun

2018-01-01

To implement the online statistical analysis function in information system of air pollution and health impact monitoring, and obtain the data analysis information real-time. Using the descriptive statistical method as well as time-series analysis and multivariate regression analysis, SQL language and visual tools to implement online statistical analysis based on database software. Generate basic statistical tables and summary tables of air pollution exposure and health impact data online; Generate tendency charts of each data part online and proceed interaction connecting to database; Generate butting sheets which can lead to R, SAS and SPSS directly online. The information system air pollution and health impact monitoring implements the statistical analysis function online, which can provide real-time analysis result to its users.

Obesity as a risk factor for developing functional limitation among older adults: A conditional inference tree analysis

USDA-ARS?s Scientific Manuscript database

Objective: To examine the risk factors of developing functional decline and make probabilistic predictions by using a tree-based method that allows higher order polynomials and interactions of the risk factors. Methods: The conditional inference tree analysis, a data mining approach, was used to con...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jason L. Wright

Finding and identifying Cryptography is a growing concern in the malware analysis community. In this paper, a heuristic method for determining the likelihood that a given function contains a cryptographic algorithm is discussed and the results of applying this method in various environments is shown. The algorithm is based on frequency analysis of opcodes that make up each function within a binary.

A Qualitative Organic Analysis that Exploits the Senses of Smell, Touch, and Sound

ERIC Educational Resources Information Center

Bromfield-Lee, Deborah C.; Oliver-Hoyo, Maria T.

2007-01-01

This laboratory experiment utilizes the characteristic aromas of some functional groups to exploit the sense of smell as a discriminating tool in an organic qualitative analysis scheme. Students differentiate a variety of compounds by their aromas and based on their olfactory classification identify an unknown functional group. Students then…

A Student's Construction of Transformations of Functions in a Multiple Representational Environment.

ERIC Educational Resources Information Center

Borba, Marcelo C.; Confrey, Jere

1996-01-01

Reports on a case study of a 16-year-old student working on transformations of functions in a computer-based, multirepresentational environment. Presents an analysis of the work during the transition from the use of visualization and analysis of discrete points to the use of algebraic symbolism. (AIM)

The physiology analysis system: an integrated approach for warehousing, management and analysis of time-series physiology data.

PubMed

McKenna, Thomas M; Bawa, Gagandeep; Kumar, Kamal; Reifman, Jaques

2007-04-01

The physiology analysis system (PAS) was developed as a resource to support the efficient warehousing, management, and analysis of physiology data, particularly, continuous time-series data that may be extensive, of variable quality, and distributed across many files. The PAS incorporates time-series data collected by many types of data-acquisition devices, and it is designed to free users from data management burdens. This Web-based system allows both discrete (attribute) and time-series (ordered) data to be manipulated, visualized, and analyzed via a client's Web browser. All processes occur on a server, so that the client does not have to download data or any application programs, and the PAS is independent of the client's computer operating system. The PAS contains a library of functions, written in different computer languages that the client can add to and use to perform specific data operations. Functions from the library are sequentially inserted into a function chain-based logical structure to construct sophisticated data operators from simple function building blocks, affording ad hoc query and analysis of time-series data. These features support advanced mining of physiology data.

Arc_Mat: a Matlab-based spatial data analysis toolbox

NASA Astrophysics Data System (ADS)

Liu, Xingjian; Lesage, James

2010-03-01

This article presents an overview of Arc_Mat, a Matlab-based spatial data analysis software package whose source code has been placed in the public domain. An earlier version of the Arc_Mat toolbox was developed to extract map polygon and database information from ESRI shapefiles and provide high quality mapping in the Matlab software environment. We discuss revisions to the toolbox that: utilize enhanced computing and graphing capabilities of more recent versions of Matlab, restructure the toolbox with object-oriented programming features, and provide more comprehensive functions for spatial data analysis. The Arc_Mat toolbox functionality includes basic choropleth mapping; exploratory spatial data analysis that provides exploratory views of spatial data through various graphs, for example, histogram, Moran scatterplot, three-dimensional scatterplot, density distribution plot, and parallel coordinate plots; and more formal spatial data modeling that draws on the extensive Spatial Econometrics Toolbox functions. A brief review of the design aspects of the revised Arc_Mat is described, and we provide some illustrative examples that highlight representative uses of the toolbox. Finally, we discuss programming with and customizing the Arc_Mat toolbox functionalities.

The relationship between nature-based tourism and autonomic nervous system function among older adults.

PubMed

Chang, Liang-Chih

2014-01-01

Nature-based tourism has recently become a topic of interest in health research. This study was aimed at examining relationships among nature-based tourism, stress, and the function of the autonomic nervous system (ANS). Three hundred and twenty-two older adults living in Taichung City, Taiwan, were selected as participants. Data were collected by a face-to-face survey that included measures of the frequency of participation in domestic and international nature-based tourism and the stress and ANS function of these participants. The data were analyzed using a path analysis. The results demonstrated that the frequency of participation in domestic nature-based tourism directly contributed to ANS function and that it also indirectly contributed to ANS function through stress reduction. Domestic nature-based tourism can directly and indirectly contribute to ANS function among older adults. Increasing the frequency of participation in domestic nature-based tourism should be considered a critical element of health programs for older adults. © 2014 International Society of Travel Medicine.

A Comparison of Measurement Equivalence Methods Based on Confirmatory Factor Analysis and Item Response Theory.

ERIC Educational Resources Information Center

Flowers, Claudia P.; Raju, Nambury S.; Oshima, T. C.

Current interest in the assessment of measurement equivalence emphasizes two methods of analysis, linear, and nonlinear procedures. This study simulated data using the graded response model to examine the performance of linear (confirmatory factor analysis or CFA) and nonlinear (item-response-theory-based differential item function or IRT-Based…

Higher-Order Theory for Functionally Graded Materials

NASA Technical Reports Server (NTRS)

Aboudi, Jacob; Pindera, Marek-Jerzy; Arnold, Steven M.

1999-01-01

This paper presents the full generalization of the Cartesian coordinate-based higher-order theory for functionally graded materials developed by the authors during the past several years. This theory circumvents the problematic use of the standard micromechanical approach, based on the concept of a representative volume element, commonly employed in the analysis of functionally graded composites by explicitly coupling the local (microstructural) and global (macrostructural) responses. The theoretical framework is based on volumetric averaging of the various field quantities, together with imposition of boundary and interfacial conditions in an average sense between the subvolumes used to characterize the composite's functionally graded microstructure. The generalization outlined herein involves extension of the theoretical framework to enable the analysis of materials characterized by spatially variable microstructures in three directions. Specialization of the generalized theoretical framework to previously published versions of the higher-order theory for materials functionally graded in one and two directions is demonstrated. In the applications part of the paper we summarize the major findings obtained with the one-directional and two-directional versions of the higher-order theory. The results illustrate both the fundamental issues related to the influence of microstructure on microscopic and macroscopic quantities governing the response of composites and the technologically important applications. A major issue addressed herein is the applicability of the classical homogenization schemes in the analysis of functionally graded materials. The technologically important applications illustrate the utility of functionally graded microstructures in tailoring the response of structural components in a variety of applications involving uniform and gradient thermomechanical loading.

ASIL determination for motorbike's Electronics Throttle Control System (ETCS) mulfunction

NASA Astrophysics Data System (ADS)

Zaman Rokhani, Fakhrul; Rahman, Muhammad Taqiuddin Abdul; Ain Kamsani, Noor; Sidek, Roslina Mohd; Saripan, M. Iqbal; Samsudin, Khairulmizam; Khair Hassan, Mohd

2017-11-01

Electronics Throttle Control System (ETCS) is the principal electronic unit in all fuel injection engine motorbike, augmenting the engine performance efficiency in comparison to the conventional carburetor based engine. ETCS is regarded as a safety-critical component, whereby ETCS malfunction can cause unintended acceleration or deceleration event, which can be hazardous to riders. In this study, Hazard Analysis and Risk Assessment, an ISO26262 functional safety standard analysis has been applied on motorbike's ETCS to determine the required automotive safety integrity level. Based on the analysis, the established automotive safety integrity level can help to derive technical and functional safety measures for ETCS development.

RNA-TVcurve: a Web server for RNA secondary structure comparison based on a multi-scale similarity of its triple vector curve representation.

PubMed

Li, Ying; Shi, Xiaohu; Liang, Yanchun; Xie, Juan; Zhang, Yu; Ma, Qin

2017-01-21

RNAs have been found to carry diverse functionalities in nature. Inferring the similarity between two given RNAs is a fundamental step to understand and interpret their functional relationship. The majority of functional RNAs show conserved secondary structures, rather than sequence conservation. Those algorithms relying on sequence-based features usually have limitations in their prediction performance. Hence, integrating RNA structure features is very critical for RNA analysis. Existing algorithms mainly fall into two categories: alignment-based and alignment-free. The alignment-free algorithms of RNA comparison usually have lower time complexity than alignment-based algorithms. An alignment-free RNA comparison algorithm was proposed, in which novel numerical representations RNA-TVcurve (triple vector curve representation) of RNA sequence and corresponding secondary structure features are provided. Then a multi-scale similarity score of two given RNAs was designed based on wavelet decomposition of their numerical representation. In support of RNA mutation and phylogenetic analysis, a web server (RNA-TVcurve) was designed based on this alignment-free RNA comparison algorithm. It provides three functional modules: 1) visualization of numerical representation of RNA secondary structure; 2) detection of single-point mutation based on secondary structure; and 3) comparison of pairwise and multiple RNA secondary structures. The inputs of the web server require RNA primary sequences, while corresponding secondary structures are optional. For the primary sequences alone, the web server can compute the secondary structures using free energy minimization algorithm in terms of RNAfold tool from Vienna RNA package. RNA-TVcurve is the first integrated web server, based on an alignment-free method, to deliver a suite of RNA analysis functions, including visualization, mutation analysis and multiple RNAs structure comparison. The comparison results with two popular RNA comparison tools, RNApdist and RNAdistance, showcased that RNA-TVcurve can efficiently capture subtle relationships among RNAs for mutation detection and non-coding RNA classification. All the relevant results were shown in an intuitive graphical manner, and can be freely downloaded from this server. RNA-TVcurve, along with test examples and detailed documents, are available at: http://ml.jlu.edu.cn/tvcurve/ .

Effects of Aquatic Therapy and Land-Based Therapy versus Land-Based Therapy Alone on Range of Motion, Edema, and Function after Hip or Knee Replacement: A Systematic Review and Meta-analysis.

PubMed

Gibson, Alison J; Shields, Nora

2015-01-01

To determine whether aquatic therapy in combination with land-based therapy improves patient outcomes after hip or knee arthroplasty compared with land-based therapy alone. For this systematic review, six online databases (MEDLINE, CINAHL, AMED, EMBASE, Cochrane, and PEDro) were searched from the earliest date available until September 2013. Controlled trials published in English in a peer-reviewed journal that compared aquatic therapy in combination with land-based therapy with land-based therapy alone were included; trial quality was assessed using the PEDro scale. Data were presented as standardized mean differences (SMDs), their associated 95% CIs, and meta-analyses. Three small trials of moderate quality were included in the qualitative analysis. Meta-analysis of two of these studies found moderate-quality evidence that aquatic therapy in combination with land-based therapy improves functional outcomes (SMD=0.53; 95% CI, 0.03-1.03), knee range of motion (measured in knee or hip arthroplasty; SMD=0.78; 95% CI, 0.27-1.29), and edema (SMD=-0.66; 95% CI, -1.16 to -0.15) compared with land-based therapy alone. The results for improved functional outcomes were not considered clinically significant. It is not possible to draw confident conclusions from this review because of the small number of studies of limited quality and the modest differences found. Further studies of sound methodological quality are required to confirm the results. Economic analysis alongside randomized controlled trials is needed to examine the cost-effectiveness of these clinical outcomes.

Network analysis reveals disrupted functional brain circuitry in drug-naive social anxiety disorder.

PubMed

Yang, Xun; Liu, Jin; Meng, Yajing; Xia, Mingrui; Cui, Zaixu; Wu, Xi; Hu, Xinyu; Zhang, Wei; Gong, Gaolang; Gong, Qiyong; Sweeney, John A; He, Yong

2017-12-07

Social anxiety disorder (SAD) is a common and disabling condition characterized by excessive fear and avoidance of public scrutiny. Psychoradiology studies have suggested that the emotional and behavior deficits in SAD are associated with abnormalities in regional brain function and functional connectivity. However, little is known about whether intrinsic functional brain networks in patients with SAD are topologically disrupted. Here, we collected resting-state fMRI data from 33 drug-naive patients with SAD and 32 healthy controls (HC), constructed functional networks with 34 predefined regions based on previous meta-analytic research with task-based fMRI in SAD, and performed network-based statistic and graph-theory analyses. The network-based statistic analysis revealed a single connected abnormal circuitry including the frontolimbic circuit (termed the "fear circuit", including the dorsolateral prefrontal cortex, ventral medial prefrontal cortex and insula) and posterior cingulate/occipital areas supporting perceptual processing. In this single altered network, patients with SAD had higher functional connectivity than HC. At the global level, graph-theory analysis revealed that the patients exhibited a lower normalized characteristic path length than HC, which suggests a disorder-related shift of network topology toward randomized configurations. SAD-related deficits in nodal degree, efficiency and participation coefficient were detected in the parahippocampal gyrus, posterior cingulate cortex, dorsolateral prefrontal cortex, insula and the calcarine sulcus. Aspects of abnormal connectivity were associated with anxiety symptoms. These findings highlight the aberrant topological organization of functional brain network organization in SAD, which provides insights into the neural mechanisms underlying excessive fear and avoidance of social interactions in patients with debilitating social anxiety. Copyright © 2017. Published by Elsevier Inc.

PreSurgMapp: a MATLAB Toolbox for Presurgical Mapping of Eloquent Functional Areas Based on Task-Related and Resting-State Functional MRI.

PubMed

Huang, Huiyuan; Ding, Zhongxiang; Mao, Dewang; Yuan, Jianhua; Zhu, Fangmei; Chen, Shuda; Xu, Yan; Lou, Lin; Feng, Xiaoyan; Qi, Le; Qiu, Wusi; Zhang, Han; Zang, Yu-Feng

2016-10-01

The main goal of brain tumor surgery is to maximize tumor resection while minimizing the risk of irreversible postoperative functional sequelae. Eloquent functional areas should be delineated preoperatively, particularly for patients with tumors near eloquent areas. Functional magnetic resonance imaging (fMRI) is a noninvasive technique that demonstrates great promise for presurgical planning. However, specialized data processing toolkits for presurgical planning remain lacking. Based on several functions in open-source software such as Statistical Parametric Mapping (SPM), Resting-State fMRI Data Analysis Toolkit (REST), Data Processing Assistant for Resting-State fMRI (DPARSF) and Multiple Independent Component Analysis (MICA), here, we introduce an open-source MATLAB toolbox named PreSurgMapp. This toolbox can reveal eloquent areas using comprehensive methods and various complementary fMRI modalities. For example, PreSurgMapp supports both model-based (general linear model, GLM, and seed correlation) and data-driven (independent component analysis, ICA) methods and processes both task-based and resting-state fMRI data. PreSurgMapp is designed for highly automatic and individualized functional mapping with a user-friendly graphical user interface (GUI) for time-saving pipeline processing. For example, sensorimotor and language-related components can be automatically identified without human input interference using an effective, accurate component identification algorithm using discriminability index. All the results generated can be further evaluated and compared by neuro-radiologists or neurosurgeons. This software has substantial value for clinical neuro-radiology and neuro-oncology, including application to patients with low- and high-grade brain tumors and those with epilepsy foci in the dominant language hemisphere who are planning to undergo a temporal lobectomy.

Mindfulness-Based Therapies in the Treatment of Functional Gastrointestinal Disorders: A Meta-Analysis

PubMed Central

2014-01-01

Background. Functional gastrointestinal disorders are highly prevalent and standard treatments are often unsatisfactory. Mindfulness-based therapy has shown benefit in conditions including chronic pain, mood, and somatization disorders. Objectives. To assess the quality and effectiveness reported in existing literature, we conducted a meta-analysis of mindfulness-based therapy in functional gastrointestinal disorders. Methods. Pubmed, EBSCO, and Cochrane databases were searched from inception to May 2014. Study inclusion criteria included randomized, controlled studies of adults using mindfulness-based therapy in the treatment of functional gastrointestinal disorders. Study quality was evaluated using the Cochrane risk of bias. Effect sizes were calculated and pooled to achieve a summary effect for the intervention on symptom severity and quality of life. Results. Of 119 records, eight articles, describing seven studies, met inclusion criteria. In six studies, significant improvements were achieved or maintained at the end of intervention or follow-up time points. The studies had an unclear or high risk of bias. Pooled effects were statistically significant for IBS severity (0.59, 95% CI 0.33 to 0.86) and quality of life (0.56, 95% CI 0.47 to 0.79). Conclusion. Studies suggest that mindfulness based interventions may provide benefit in functional gastrointestinal disorders; however, substantial improvements in methodological quality and reporting are needed. PMID:25295066

Earth Observatory Satellite system definition study. Report no. 3: Design/cost tradeoff studies. Appendix A: EOS program WBS dictionary. Appendix B: EOS mission functional analysis

NASA Technical Reports Server (NTRS)

1974-01-01

The work breakdown structure (WBS) dictionary for the Earth Observatory Satellite (EOS) is defined. The various elements of the EOS program are examined to include the aggregate of hardware, computer software, services, and data required to develop, produce, test, support, and operate the space vehicle and the companion ground data management system. A functional analysis of the EOS mission is developed. The operations for three typical EOS missions, Delta, Titan, and Shuttle launched are considered. The functions were determined for the top program elements, and the mission operations, function 2.0, was expanded to level one functions. Selection of ten level one functions for further analysis to level two and three functions were based on concern for the EOS operations and associated interfaces.

Computer-based quantitative computed tomography image analysis in idiopathic pulmonary fibrosis: A mini review.

PubMed

Ohkubo, Hirotsugu; Nakagawa, Hiroaki; Niimi, Akio

2018-01-01

Idiopathic pulmonary fibrosis (IPF) is the most common type of progressive idiopathic interstitial pneumonia in adults. Many computer-based image analysis methods of chest computed tomography (CT) used in patients with IPF include the mean CT value of the whole lungs, density histogram analysis, density mask technique, and texture classification methods. Most of these methods offer good assessment of pulmonary functions, disease progression, and mortality. Each method has merits that can be used in clinical practice. One of the texture classification methods is reported to be superior to visual CT scoring by radiologist for correlation with pulmonary function and prediction of mortality. In this mini review, we summarize the current literature on computer-based CT image analysis of IPF and discuss its limitations and several future directions. Copyright © 2017 The Japanese Respiratory Society. Published by Elsevier B.V. All rights reserved.

Functional magnetic resonance imaging phase synchronization as a measure of dynamic functional connectivity.

PubMed

Glerean, Enrico; Salmi, Juha; Lahnakoski, Juha M; Jääskeläinen, Iiro P; Sams, Mikko

2012-01-01

Functional brain activity and connectivity have been studied by calculating intersubject and seed-based correlations of hemodynamic data acquired with functional magnetic resonance imaging (fMRI). To inspect temporal dynamics, these correlation measures have been calculated over sliding time windows with necessary restrictions on the length of the temporal window that compromises the temporal resolution. Here, we show that it is possible to increase temporal resolution by using instantaneous phase synchronization (PS) as a measure of dynamic (time-varying) functional connectivity. We applied PS on an fMRI dataset obtained while 12 healthy volunteers watched a feature film. Narrow frequency band (0.04-0.07 Hz) was used in the PS analysis to avoid artifactual results. We defined three metrics for computing time-varying functional connectivity and time-varying intersubject reliability based on estimation of instantaneous PS across the subjects: (1) seed-based PS, (2) intersubject PS, and (3) intersubject seed-based PS. Our findings show that these PS-based metrics yield results consistent with both seed-based correlation and intersubject correlation methods when inspected over the whole time series, but provide an important advantage of maximal single-TR temporal resolution. These metrics can be applied both in studies with complex naturalistic stimuli (e.g., watching a movie or listening to music in the MRI scanner) and more controlled (e.g., event-related or blocked design) paradigms. A MATLAB toolbox FUNPSY ( http://becs.aalto.fi/bml/software.html ) is openly available for using these metrics in fMRI data analysis.

A new methodology based on functional principal component analysis to study postural stability post-stroke.

PubMed

Sánchez-Sánchez, M Luz; Belda-Lois, Juan-Manuel; Mena-Del Horno, Silvia; Viosca-Herrero, Enrique; Igual-Camacho, Celedonia; Gisbert-Morant, Beatriz

2018-05-05

A major goal in stroke rehabilitation is the establishment of more effective physical therapy techniques to recover postural stability. Functional Principal Component Analysis provides greater insight into recovery trends. However, when missing values exist, obtaining functional data presents some difficulties. The purpose of this study was to reveal an alternative technique for obtaining the Functional Principal Components without requiring the conversion to functional data beforehand and to investigate this methodology to determine the effect of specific physical therapy techniques in balance recovery trends in elderly subjects with hemiplegia post-stroke. A randomized controlled pilot trial was developed. Thirty inpatients post-stroke were included. Control and target groups were treated with the same conventional physical therapy protocol based on functional criteria, but specific techniques were added to the target group depending on the subjects' functional level. Postural stability during standing was quantified by posturography. The assessments were performed once a month from the moment the participants were able to stand up to six months post-stroke. The target group showed a significant improvement in postural control recovery trend six months after stroke that was not present in the control group. Some of the assessed parameters revealed significant differences between treatment groups (P < 0.05). The proposed methodology allows Functional Principal Component Analysis to be performed when data is scarce. Moreover, it allowed the dynamics of recovery of two different treatment groups to be determined, showing that the techniques added in the target group increased postural stability compared to the base protocol. Copyright © 2018 Elsevier Ltd. All rights reserved.

atBioNet--an integrated network analysis tool for genomics and biomarker discovery.

PubMed

Ding, Yijun; Chen, Minjun; Liu, Zhichao; Ding, Don; Ye, Yanbin; Zhang, Min; Kelly, Reagan; Guo, Li; Su, Zhenqiang; Harris, Stephen C; Qian, Feng; Ge, Weigong; Fang, Hong; Xu, Xiaowei; Tong, Weida

2012-07-20

Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

Integrative analysis of environmental sequences using MEGAN4.

PubMed

Huson, Daniel H; Mitra, Suparna; Ruscheweyh, Hans-Joachim; Weber, Nico; Schuster, Stephan C

2011-09-01

A major challenge in the analysis of environmental sequences is data integration. The question is how to analyze different types of data in a unified approach, addressing both the taxonomic and functional aspects. To facilitate such analyses, we have substantially extended MEGAN, a widely used taxonomic analysis program. The new program, MEGAN4, provides an integrated approach to the taxonomic and functional analysis of metagenomic, metatranscriptomic, metaproteomic, and rRNA data. While taxonomic analysis is performed based on the NCBI taxonomy, functional analysis is performed using the SEED classification of subsystems and functional roles or the KEGG classification of pathways and enzymes. A number of examples illustrate how such analyses can be performed, and show that one can also import and compare classification results obtained using others' tools. MEGAN4 is freely available for academic purposes, and installers for all three major operating systems can be downloaded from www-ab.informatik.uni-tuebingen.de/software/megan.

Rapid automatic keyword extraction for information retrieval and analysis

DOEpatents

Rose, Stuart J [Richland, WA; Cowley,; E, Wendy [Richland, WA; Crow, Vernon L [Richland, WA; Cramer, Nicholas O [Richland, WA

2012-03-06

Methods and systems for rapid automatic keyword extraction for information retrieval and analysis. Embodiments can include parsing words in an individual document by delimiters, stop words, or both in order to identify candidate keywords. Word scores for each word within the candidate keywords are then calculated based on a function of co-occurrence degree, co-occurrence frequency, or both. Based on a function of the word scores for words within the candidate keyword, a keyword score is calculated for each of the candidate keywords. A portion of the candidate keywords are then extracted as keywords based, at least in part, on the candidate keywords having the highest keyword scores.

An exploratory data analysis of electroencephalograms using the functional boxplots approach

PubMed Central

Ngo, Duy; Sun, Ying; Genton, Marc G.; Wu, Jennifer; Srinivasan, Ramesh; Cramer, Steven C.; Ombao, Hernando

2015-01-01

Many model-based methods have been developed over the last several decades for analysis of electroencephalograms (EEGs) in order to understand electrical neural data. In this work, we propose to use the functional boxplot (FBP) to analyze log periodograms of EEG time series data in the spectral domain. The functional bloxplot approach produces a median curve—which is not equivalent to connecting medians obtained from frequency-specific boxplots. In addition, this approach identifies a functional median, summarizes variability, and detects potential outliers. By extending FBPs analysis from one-dimensional curves to surfaces, surface boxplots are also used to explore the variation of the spectral power for the alpha (8–12 Hz) and beta (16–32 Hz) frequency bands across the brain cortical surface. By using rank-based nonparametric tests, we also investigate the stationarity of EEG traces across an exam acquired during resting-state by comparing the spectrum during the early vs. late phases of a single resting-state EEG exam. PMID:26347598

Satellite services system analysis study. Volume 1, part 2: Executive summary

NASA Technical Reports Server (NTRS)

1981-01-01

The early mission model was developed through a survey of the potential user market. Service functions were defined and a group of design reference missions were selected which represented needs for each of the service functions. Servicing concepts were developed through mission analysis and STS timeline constraint analysis. The hardware needs for accomplishing the service functions were identified with emphasis being placed on applying equipment in the current NASA inventory and that in advanced stages of planning. A more comprehensive service model was developed based on the NASA and DoD mission models segregated by mission class. The number of service events of each class were estimated based on average revisit and service assumptions. Service Kits were defined as collections of equipment applicable to performing one or more service functions. Preliminary design was carrid out on a selected set of hardware needed for early service missions. The organization and costing of the satellie service systems were addressed.

Structure-based Markov random field model for representing evolutionary constraints on functional sites.

PubMed

Jeong, Chan-Seok; Kim, Dongsup

2016-02-24

Elucidating the cooperative mechanism of interconnected residues is an important component toward understanding the biological function of a protein. Coevolution analysis has been developed to model the coevolutionary information reflecting structural and functional constraints. Recently, several methods have been developed based on a probabilistic graphical model called the Markov random field (MRF), which have led to significant improvements for coevolution analysis; however, thus far, the performance of these models has mainly been assessed by focusing on the aspect of protein structure. In this study, we built an MRF model whose graphical topology is determined by the residue proximity in the protein structure, and derived a novel positional coevolution estimate utilizing the node weight of the MRF model. This structure-based MRF method was evaluated for three data sets, each of which annotates catalytic site, allosteric site, and comprehensively determined functional site information. We demonstrate that the structure-based MRF architecture can encode the evolutionary information associated with biological function. Furthermore, we show that the node weight can more accurately represent positional coevolution information compared to the edge weight. Lastly, we demonstrate that the structure-based MRF model can be reliably built with only a few aligned sequences in linear time. The results show that adoption of a structure-based architecture could be an acceptable approximation for coevolution modeling with efficient computation complexity.

Multifractal analysis with the probability density function at the three-dimensional anderson transition.

PubMed

Rodriguez, Alberto; Vasquez, Louella J; Römer, Rudolf A

2009-03-13

The probability density function (PDF) for critical wave function amplitudes is studied in the three-dimensional Anderson model. We present a formal expression between the PDF and the multifractal spectrum f(alpha) in which the role of finite-size corrections is properly analyzed. We show the non-Gaussian nature and the existence of a symmetry relation in the PDF. From the PDF, we extract information about f(alpha) at criticality such as the presence of negative fractal dimensions and the possible existence of termination points. A PDF-based multifractal analysis is shown to be a valid alternative to the standard approach based on the scaling of inverse participation ratios.

Assessing prescription drug abuse using functional principal component analysis (FPCA) of wastewater data.

PubMed

Salvatore, Stefania; Røislien, Jo; Baz-Lomba, Jose A; Bramness, Jørgen G

2017-03-01

Wastewater-based epidemiology is an alternative method for estimating the collective drug use in a community. We applied functional data analysis, a statistical framework developed for analysing curve data, to investigate weekly temporal patterns in wastewater measurements of three prescription drugs with known abuse potential: methadone, oxazepam and methylphenidate, comparing them to positive and negative control drugs. Sewage samples were collected in February 2014 from a wastewater treatment plant in Oslo, Norway. The weekly pattern of each drug was extracted by fitting of generalized additive models, using trigonometric functions to model the cyclic behaviour. From the weekly component, the main temporal features were then extracted using functional principal component analysis. Results are presented through the functional principal components (FPCs) and corresponding FPC scores. Clinically, the most important weekly feature of the wastewater-based epidemiology data was the second FPC, representing the difference between average midweek level and a peak during the weekend, representing possible recreational use of a drug in the weekend. Estimated scores on this FPC indicated recreational use of methylphenidate, with a high weekend peak, but not for methadone and oxazepam. The functional principal component analysis uncovered clinically important temporal features of the weekly patterns of the use of prescription drugs detected from wastewater analysis. This may be used as a post-marketing surveillance method to monitor prescription drugs with abuse potential. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Broad-Based National Education in Globalisation: Conceptualisation, Multiple Functions and Management

ERIC Educational Resources Information Center

Cheng, Yin Cheong; Yuen, Timothy W. W.

2017-01-01

Purpose: The purpose of this paper is to contribute to the worldwide discussion of conceptualization, multiple functions and management of national education in an era of globalisation by proposing a new comprehensive framework for research, policy analysis and practical implementation. Design/Methodology/Approach: Based on a review of the…

School-Based Functional Assessments for Children with Physical Disabilities in Grades K-12

ERIC Educational Resources Information Center

Johnson, Richard W.

2012-01-01

The purpose of this study was to develop three school-based assessments and determine the content validity for each assessment. The School Activities and Participation Analysis-Elementary (SAPA-E) measures functional movement performance in children with physical disability attending the elementary school, and the School Activities and…

Tensor Based Representation and Analysis of Diffusion-Weighted Magnetic Resonance Images

ERIC Educational Resources Information Center

Barmpoutis, Angelos

2009-01-01

Cartesian tensor bases have been widely used to model spherical functions. In medical imaging, tensors of various orders can approximate the diffusivity function at each voxel of a diffusion-weighted MRI data set. This approximation produces tensor-valued datasets that contain information about the underlying local structure of the scanned tissue.…

Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated "Knowledge-Based" Platform.

PubMed

Dubovenko, Alexey; Nikolsky, Yuri; Rakhmatulin, Eugene; Nikolskaya, Tatiana

2017-01-01

Analysis of NGS and other sequencing data, gene variants, gene expression, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high fidelity annotated knowledgebase of protein interactions, pathways, and functional ontologies. This knowledgebase has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here, we present MetaCore™ and Key Pathway Advisor (KPA), an integrated platform for functional data analysis. On the content side, MetaCore and KPA encompass a comprehensive database of molecular interactions of different types, pathways, network models, and ten functional ontologies covering human, mouse, and rat genes. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for the identification of over- and under-connected proteins in the dataset, and a biological network analysis module made up of network generation algorithms and filters. The suite also features Advanced Search, an application for combinatorial search of the database content, as well as a Java-based tool called Pathway Map Creator for drawing and editing custom pathway maps. Applications of MetaCore and KPA include molecular mode of action of disease research, identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds and clinical applications (analysis of large cohorts of patients, and translational and personalized medicine).

DataHub knowledge based assistance for science visualization and analysis using large distributed databases

NASA Technical Reports Server (NTRS)

Handley, Thomas H., Jr.; Collins, Donald J.; Doyle, Richard J.; Jacobson, Allan S.

1991-01-01

Viewgraphs on DataHub knowledge based assistance for science visualization and analysis using large distributed databases. Topics covered include: DataHub functional architecture; data representation; logical access methods; preliminary software architecture; LinkWinds; data knowledge issues; expert systems; and data management.

A step-by-step guide to non-linear regression analysis of experimental data using a Microsoft Excel spreadsheet.

PubMed

Brown, A M

2001-06-01

The objective of this present study was to introduce a simple, easily understood method for carrying out non-linear regression analysis based on user input functions. While it is relatively straightforward to fit data with simple functions such as linear or logarithmic functions, fitting data with more complicated non-linear functions is more difficult. Commercial specialist programmes are available that will carry out this analysis, but these programmes are expensive and are not intuitive to learn. An alternative method described here is to use the SOLVER function of the ubiquitous spreadsheet programme Microsoft Excel, which employs an iterative least squares fitting routine to produce the optimal goodness of fit between data and function. The intent of this paper is to lead the reader through an easily understood step-by-step guide to implementing this method, which can be applied to any function in the form y=f(x), and is well suited to fast, reliable analysis of data in all fields of biology.

A human functional protein interaction network and its application to cancer data analysis

PubMed Central

2010-01-01

Background One challenge facing biologists is to tease out useful information from massive data sets for further analysis. A pathway-based analysis may shed light by projecting candidate genes onto protein functional relationship networks. We are building such a pathway-based analysis system. Results We have constructed a protein functional interaction network by extending curated pathways with non-curated sources of information, including protein-protein interactions, gene coexpression, protein domain interaction, Gene Ontology (GO) annotations and text-mined protein interactions, which cover close to 50% of the human proteome. By applying this network to two glioblastoma multiforme (GBM) data sets and projecting cancer candidate genes onto the network, we found that the majority of GBM candidate genes form a cluster and are closer than expected by chance, and the majority of GBM samples have sequence-altered genes in two network modules, one mainly comprising genes whose products are localized in the cytoplasm and plasma membrane, and another comprising gene products in the nucleus. Both modules are highly enriched in known oncogenes, tumor suppressors and genes involved in signal transduction. Similar network patterns were also found in breast, colorectal and pancreatic cancers. Conclusions We have built a highly reliable functional interaction network upon expert-curated pathways and applied this network to the analysis of two genome-wide GBM and several other cancer data sets. The network patterns revealed from our results suggest common mechanisms in the cancer biology. Our system should provide a foundation for a network or pathway-based analysis platform for cancer and other diseases. PMID:20482850

Characterization of Microbiota in Children with Chronic Functional Constipation.

PubMed

de Meij, Tim G J; de Groot, Evelien F J; Eck, Anat; Budding, Andries E; Kneepkens, C M Frank; Benninga, Marc A; van Bodegraven, Adriaan A; Savelkoul, Paul H M

2016-01-01

Disruption of the intestinal microbiota is considered an etiological factor in pediatric functional constipation. Scientifically based selection of potential beneficial probiotic strains in functional constipation therapy is not feasible due to insufficient knowledge of microbiota composition in affected subjects. The aim of this study was to describe microbial composition and diversity in children with functional constipation, compared to healthy controls. Fecal samples from 76 children diagnosed with functional constipation according to the Rome III criteria (median age 8.0 years; range 4.2-17.8) were analyzed by IS-pro, a PCR-based microbiota profiling method. Outcome was compared with intestinal microbiota profiles of 61 healthy children (median 8.6 years; range 4.1-17.9). Microbiota dissimilarity was depicted by principal coordinate analysis (PCoA), diversity was calculated by Shannon diversity index. To determine the most discriminative species, cross validated logistic ridge regression was performed. Applying total microbiota profiles (all phyla together) or per phylum analysis, no disease-specific separation was observed by PCoA and by calculation of diversity indices. By ridge regression, however, functional constipation and controls could be discriminated with 82% accuracy. Most discriminative species were Bacteroides fragilis, Bacteroides ovatus, Bifidobacterium longum, Parabacteroides species (increased in functional constipation) and Alistipes finegoldii (decreased in functional constipation). None of the commonly used unsupervised statistical methods allowed for microbiota-based discrimination of children with functional constipation and controls. By ridge regression, however, both groups could be discriminated with 82% accuracy. Optimization of microbiota-based interventions in constipated children warrants further characterization of microbial signatures linked to clinical subgroups of functional constipation.

Using Molecular Visualization to Explore Protein Structure and Function and Enhance Student Facility with Computational Tools

ERIC Educational Resources Information Center

Terrell, Cassidy R.; Listenberger, Laura L.

2017-01-01

Recognizing that undergraduate students can benefit from analysis of 3D protein structure and function, we have developed a multiweek, inquiry-based molecular visualization project for Biochemistry I students. This project uses a virtual model of cyclooxygenase-1 (COX-1) to guide students through multiple levels of protein structure analysis. The…

Incorporating "Motivation" into the Functional Analysis of Challenging Behavior: On the Interactive and Integrative Potential of the Motivating Operation

ERIC Educational Resources Information Center

Langthorne, Paul; McGill, Peter; O'Reilly, Mark

2007-01-01

Sensitivity theory attempts to account for the variability often observed in challenging behavior by recourse to the "aberrant motivation" of people with intellectual and developmental disabilities. In this article, we suggest that a functional analysis based on environmental (challenging environments) and biological (challenging needs) motivating…

Evolution-Based Functional Decomposition of Proteins

PubMed Central

Rivoire, Olivier; Reynolds, Kimberly A.; Ranganathan, Rama

2016-01-01

The essential biological properties of proteins—folding, biochemical activities, and the capacity to adapt—arise from the global pattern of interactions between amino acid residues. The statistical coupling analysis (SCA) is an approach to defining this pattern that involves the study of amino acid coevolution in an ensemble of sequences comprising a protein family. This approach indicates a functional architecture within proteins in which the basic units are coupled networks of amino acids termed sectors. This evolution-based decomposition has potential for new understandings of the structural basis for protein function. To facilitate its usage, we present here the principles and practice of the SCA and introduce new methods for sector analysis in a python-based software package (pySCA). We show that the pattern of amino acid interactions within sectors is linked to the divergence of functional lineages in a multiple sequence alignment—a model for how sector properties might be differentially tuned in members of a protein family. This work provides new tools for studying proteins and for generally testing the concept of sectors as the principal units of function and adaptive variation. PMID:27254668

NEAT: an efficient network enrichment analysis test.

PubMed

Signorelli, Mirko; Vinciotti, Veronica; Wit, Ernst C

2016-09-05

Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves. NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat ).

Functional connectivity decreases in autism in emotion, self, and face circuits identified by Knowledge-based Enrichment Analysis.

PubMed

Cheng, Wei; Rolls, Edmund T; Zhang, Jie; Sheng, Wenbo; Ma, Liang; Wan, Lin; Luo, Qiang; Feng, Jianfeng

2017-03-01

A powerful new method is described called Knowledge based functional connectivity Enrichment Analysis (KEA) for interpreting resting state functional connectivity, using circuits that are functionally identified using search terms with the Neurosynth database. The method derives its power by focusing on neural circuits, sets of brain regions that share a common biological function, instead of trying to interpret single functional connectivity links. This provides a novel way of investigating how task- or function-related networks have resting state functional connectivity differences in different psychiatric states, provides a new way to bridge the gap between task and resting-state functional networks, and potentially helps to identify brain networks that might be treated. The method was applied to interpreting functional connectivity differences in autism. Functional connectivity decreases at the network circuit level in 394 patients with autism compared with 473 controls were found in networks involving the orbitofrontal cortex, anterior cingulate cortex, middle temporal gyrus cortex, and the precuneus, in networks that are implicated in the sense of self, face processing, and theory of mind. The decreases were correlated with symptom severity. Copyright © 2017. Published by Elsevier Inc.

Effects of gross motor function and manual function levels on performance-based ADL motor skills of children with spastic cerebral palsy.

PubMed

Park, Myoung-Ok

2017-02-01

[Purpose] The purpose of this study was to determine effects of Gross Motor Function Classification System and Manual Ability Classification System levels on performance-based motor skills of children with spastic cerebral palsy. [Subjects and Methods] Twenty-three children with cerebral palsy were included. The Assessment of Motor and Process Skills was used to evaluate performance-based motor skills in daily life. Gross motor function was assessed using Gross Motor Function Classification Systems, and manual function was measured using the Manual Ability Classification System. [Results] Motor skills in daily activities were significantly different on Gross Motor Function Classification System level and Manual Ability Classification System level. According to the results of multiple regression analysis, children categorized as Gross Motor Function Classification System level III scored lower in terms of performance based motor skills than Gross Motor Function Classification System level I children. Also, when analyzed with respect to Manual Ability Classification System level, level II was lower than level I, and level III was lower than level II in terms of performance based motor skills. [Conclusion] The results of this study indicate that performance-based motor skills differ among children categorized based on Gross Motor Function Classification System and Manual Ability Classification System levels of cerebral palsy.

Operational characterisation of requirements and early validation environment for high demanding space systems

NASA Technical Reports Server (NTRS)

Barro, E.; Delbufalo, A.; Rossi, F.

1993-01-01

The definition of some modern high demanding space systems requires a different approach to system definition and design from that adopted for traditional missions. System functionality is strongly coupled to the operational analysis, aimed at characterizing the dynamic interactions of the flight element with its surrounding environment and its ground control segment. Unambiguous functional, operational and performance requirements are to be defined for the system, thus improving also the successive development stages. This paper proposes a Petri Nets based methodology and two related prototype applications (to ARISTOTELES orbit control and to Hermes telemetry generation) for the operational analysis of space systems through the dynamic modeling of their functions and a related computer aided environment (ISIDE) able to make the dynamic model work, thus enabling an early validation of the system functional representation, and to provide a structured system requirements data base, which is the shared knowledge base interconnecting static and dynamic applications, fully traceable with the models and interfaceable with the external world.

Iberian Spanish Function Catalog. Method for Determining Language Objectives and Criteria, Volume VI.

ERIC Educational Resources Information Center

Setzler, Hubert H., Jr.; And Others

This Iberian Spanish Function Catalog presents sentences, phrases, and patterns organized by language functions and functional categories. This catalog is part of the communication/language objectives-based system (C/LOBS), which supports the front-end analysis efforts of the Defense Language Institute Foreign Language Center. The C/LOBS project,…

Influence of CT-based depth correction of renal scintigraphy in evaluation of living kidney donors on side selection and postoperative renal function: is it necessary to know the relative renal function?

PubMed

Weinberger, Sarah; Klarholz-Pevere, Carola; Liefeldt, Lutz; Baeder, Michael; Steckhan, Nico; Friedersdorff, Frank

2018-03-22

To analyse the influence of CT-based depth correction in the assessment of split renal function in potential living kidney donors. In 116 consecutive living kidney donors preoperative split renal function was assessed using the CT-based depth correction. Influence on donor side selection and postoperative renal function of the living kidney donors were analyzed. Linear regression analysis was performed to identify predictors of postoperative renal function. A left versus right kidney depth variation of more than 1 cm was found in 40/114 donors (35%). 11 patients (10%) had a difference of more than 5% in relative renal function after depth correction. Kidney depth variation and changes in relative renal function after depth correction would have had influence on side selection in 30 of 114 living kidney donors. CT depth correction did not improve the predictability of postoperative renal function of the living kidney donor. In general, it was not possible to predict the postoperative renal function from preoperative total and relative renal function. In multivariate linear regression analysis, age and BMI were identified as most important predictors for postoperative renal function of the living kidney donors. Our results clearly indicate that concerning the postoperative renal function of living kidney donors, the relative renal function of the donated kidney seems to be less important than other factors. A multimodal assessment with consideration of all available results including kidney size, location of the kidney and split renal function remains necessary.

Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction

PubMed Central

2014-01-01

Background Network-based learning algorithms for automated function prediction (AFP) are negatively affected by the limited coverage of experimental data and limited a priori known functional annotations. As a consequence their application to model organisms is often restricted to well characterized biological processes and pathways, and their effectiveness with poorly annotated species is relatively limited. A possible solution to this problem might consist in the construction of big networks including multiple species, but this in turn poses challenging computational problems, due to the scalability limitations of existing algorithms and the main memory requirements induced by the construction of big networks. Distributed computation or the usage of big computers could in principle respond to these issues, but raises further algorithmic problems and require resources not satisfiable with simple off-the-shelf computers. Results We propose a novel framework for scalable network-based learning of multi-species protein functions based on both a local implementation of existing algorithms and the adoption of innovative technologies: we solve “locally” the AFP problem, by designing “vertex-centric” implementations of network-based algorithms, but we do not give up thinking “globally” by exploiting the overall topology of the network. This is made possible by the adoption of secondary memory-based technologies that allow the efficient use of the large memory available on disks, thus overcoming the main memory limitations of modern off-the-shelf computers. This approach has been applied to the analysis of a large multi-species network including more than 300 species of bacteria and to a network with more than 200,000 proteins belonging to 13 Eukaryotic species. To our knowledge this is the first work where secondary-memory based network analysis has been applied to multi-species function prediction using biological networks with hundreds of thousands of proteins. Conclusions The combination of these algorithmic and technological approaches makes feasible the analysis of large multi-species networks using ordinary computers with limited speed and primary memory, and in perspective could enable the analysis of huge networks (e.g. the whole proteomes available in SwissProt), using well-equipped stand-alone machines. PMID:24843788

Clinical relevance of gait research applied to clinical trials in spinal cord injury.

PubMed

Ditunno, John; Scivoletto, Giorgio

2009-01-15

The restoration of walking function following SCI is extremely important to consumers and has stimulated a response of new treatments by scientists, the pharmaceutical industry and clinical entrepreneurs. Several of the proposed interventions: (1) the use of functional electrical stimulation (FES) and (2) locomotor training have been examined in clinical trials and recent reviews of the scientific literature. Each of these interventions is based on research of human locomotion. Therefore, the systematic study of walking function and gait in normal individuals and those with injury to the spinal cord has contributed to the identification of the impairments of walking, the development of new treatments and how they will be measured to determine effectiveness. In this context gait research applied to interventions to improve walking function is of high clinical relevance. This research helps identify walking impairments to be corrected and measures of walking function to be utilized as endpoints for clinical trials. The most common impairments following SCI diagnosed by observational gait analysis include inadequate hip extension during stance, persistent plantar flexion and hip/knee flexion during swing and foot placement at heel strike. FES has been employed as one strategy for correcting these impairments based on analysis that range from simple measures of speed, cadence and stride length to more sophisticated systems of three- dimensional video motion analysis and multichannel EMG tracings of integrated walking. A recent review of the entire FES literature identified 36 studies that merit comment and the full range of outcome measures for walking function were used from simple velocity to the video analysis of motion. In addition to measures of walking function developed for FES interventions, the first randomized multicenter clinical trial on locomotor training in subacute SCI was recently published with an extensive review of these measures. In this study outcome measures of motor strength (impairment), balance, Walking Index for SCI (WISCI), speed, 5min walk (walking capacities) and locomotor functional independence measure (L-FIM), a disability measure all showed improvement in walking function based on the strategy of the response of activity based plasticity to step training. Although the scientific basis for this intervention will be covered in other articles in this series, the evolution of clinical outcome measures of walking function continues to be important for the determination of effectiveness in clinical trials.

A "Healthy-Contingencies" Behavioral Intervention

ERIC Educational Resources Information Center

St. Peter, Claire C.; Marsteller, Tonya M.

2017-01-01

Interventions based on functional analyses may result in better treatment outcomes than those using arbitrary reinforcers. However, functional analyses may be impractical in some situations, or an immediate intervention may be necessary while a functional analysis is being conducted. In these situations, delivering the social reinforcers most…

A Parallel Product-Convolution approach for representing the depth varying Point Spread Functions in 3D widefield microscopy based on principal component analysis.

PubMed

Arigovindan, Muthuvel; Shaevitz, Joshua; McGowan, John; Sedat, John W; Agard, David A

2010-03-29

We address the problem of computational representation of image formation in 3D widefield fluorescence microscopy with depth varying spherical aberrations. We first represent 3D depth-dependent point spread functions (PSFs) as a weighted sum of basis functions that are obtained by principal component analysis (PCA) of experimental data. This representation is then used to derive an approximating structure that compactly expresses the depth variant response as a sum of few depth invariant convolutions pre-multiplied by a set of 1D depth functions, where the convolving functions are the PCA-derived basis functions. The model offers an efficient and convenient trade-off between complexity and accuracy. For a given number of approximating PSFs, the proposed method results in a much better accuracy than the strata based approximation scheme that is currently used in the literature. In addition to yielding better accuracy, the proposed methods automatically eliminate the noise in the measured PSFs.

Use of a latency-based demand assessment to identify potential demands for functional analyses.

PubMed

Call, Nathan A; Miller, Sarah J; Mintz, Joslyn Cynkus; Mevers, Joanna Lomas; Scheithauer, Mindy C; Eshelman, Julie E; Beavers, Gracie A

2016-12-01

Unlike potential tangible positive reinforcers, which are typically identified for inclusion in functional analyses empirically using preference assessments, demands are most often selected arbitrarily or based on caregiver report. The present study evaluated the use of a demand assessment with 12 participants who exhibited escape-maintained problem behavior. Participants were exposed to 10 demands, with aversiveness measured by average latency to the first instance of problem behavior. In subsequent functional analyses, results of a demand condition that included the demand with the shortest latency to problem behavior resulted in identification of an escape function for 11 of the participants. In contrast, a demand condition that included the demand with the longest latency resulted in identification of an escape function for only 5 participants. The implication of these findings is that for the remaining 7 participants, selection of the demand for the functional analysis without using the results of the demand assessment could have produced a false-negative finding. © 2016 Society for the Experimental Analysis of Behavior.

Input-output Transfer Function Analysis of a Photometer Circuit Based on an Operational Amplifier.

PubMed

Hernandez, Wilmar

2008-01-09

In this paper an input-output transfer function analysis based on the frequencyresponse of a photometer circuit based on operational amplifier (op amp) is carried out. Opamps are universally used in monitoring photodetectors and there are a variety of amplifierconnections for this purpose. However, the electronic circuits that are usually used to carryout the signal treatment in photometer circuits introduce some limitations in theperformance of the photometers that influence the selection of the op amps and otherelectronic devices. For example, the bandwidth, slew-rate, noise, input impedance and gain,among other characteristics of the op amp, are often the performance limiting factors ofphotometer circuits. For this reason, in this paper a comparative analysis between twophotodiode amplifier circuits is carried out. One circuit is based on a conventional currentto-voltage converter connection and the other circuit is based on a robust current-to-voltageconverter connection. The results are satisfactory and show that the photodiode amplifierperformance can be improved by using robust control techniques.

Loop transfer recovery for general nonminimum phase discrete time systems. I - Analysis

NASA Technical Reports Server (NTRS)

Chen, Ben M.; Saberi, Ali; Sannuti, Peddapullaiah; Shamash, Yacov

1992-01-01

A complete analysis of loop transfer recovery (LTR) for general nonstrictly proper, not necessarily minimum phase discrete time systems is presented. Three different observer-based controllers, namely, `prediction estimator' and full or reduced-order type `current estimator' based controllers, are used. The analysis corresponding to all these three controllers is unified into a single mathematical framework. The LTR analysis given here focuses on three fundamental issues: (1) the recoverability of a target loop when it is arbitrarily given, (2) the recoverability of a target loop while taking into account its specific characteristics, and (3) the establishment of necessary and sufficient conditions on the given system so that it has at least one recoverable target loop transfer function or sensitivity function. Various differences that arise in LTR analysis of continuous and discrete systems are pointed out.

Specialization and integration of functional thalamocortical connectivity in the human infant.

PubMed

Toulmin, Hilary; Beckmann, Christian F; O'Muircheartaigh, Jonathan; Ball, Gareth; Nongena, Pumza; Makropoulos, Antonios; Ederies, Ashraf; Counsell, Serena J; Kennea, Nigel; Arichi, Tomoki; Tusor, Nora; Rutherford, Mary A; Azzopardi, Denis; Gonzalez-Cinca, Nuria; Hajnal, Joseph V; Edwards, A David

2015-05-19

Connections between the thalamus and cortex develop rapidly before birth, and aberrant cerebral maturation during this period may underlie a number of neurodevelopmental disorders. To define functional thalamocortical connectivity at the normal time of birth, we used functional MRI (fMRI) to measure blood oxygen level-dependent (BOLD) signals in 66 infants, 47 of whom were at high risk of neurocognitive impairment because of birth before 33 wk of gestation and 19 of whom were term infants. We segmented the thalamus based on correlation with functionally defined cortical components using independent component analysis (ICA) and seed-based correlations. After parcellating the cortex using ICA and segmenting the thalamus based on dominant connections with cortical parcellations, we observed a near-facsimile of the adult functional parcellation. Additional analysis revealed that BOLD signal in heteromodal association cortex typically had more widespread and overlapping thalamic representations than primary sensory cortex. Notably, more extreme prematurity was associated with increased functional connectivity between thalamus and lateral primary sensory cortex but reduced connectivity between thalamus and cortex in the prefrontal, insular and anterior cingulate regions. This work suggests that, in early infancy, functional integration through thalamocortical connections depends on significant functional overlap in the topographic organization of the thalamus and that the experience of premature extrauterine life modulates network development, altering the maturation of networks thought to support salience, executive, integrative, and cognitive functions.

Specialization and integration of functional thalamocortical connectivity in the human infant

PubMed Central

Toulmin, Hilary; Beckmann, Christian F.; O'Muircheartaigh, Jonathan; Ball, Gareth; Nongena, Pumza; Makropoulos, Antonios; Ederies, Ashraf; Counsell, Serena J.; Kennea, Nigel; Arichi, Tomoki; Tusor, Nora; Rutherford, Mary A.; Azzopardi, Denis; Gonzalez-Cinca, Nuria; Hajnal, Joseph V.; Edwards, A. David

2015-01-01

Connections between the thalamus and cortex develop rapidly before birth, and aberrant cerebral maturation during this period may underlie a number of neurodevelopmental disorders. To define functional thalamocortical connectivity at the normal time of birth, we used functional MRI (fMRI) to measure blood oxygen level-dependent (BOLD) signals in 66 infants, 47 of whom were at high risk of neurocognitive impairment because of birth before 33 wk of gestation and 19 of whom were term infants. We segmented the thalamus based on correlation with functionally defined cortical components using independent component analysis (ICA) and seed-based correlations. After parcellating the cortex using ICA and segmenting the thalamus based on dominant connections with cortical parcellations, we observed a near-facsimile of the adult functional parcellation. Additional analysis revealed that BOLD signal in heteromodal association cortex typically had more widespread and overlapping thalamic representations than primary sensory cortex. Notably, more extreme prematurity was associated with increased functional connectivity between thalamus and lateral primary sensory cortex but reduced connectivity between thalamus and cortex in the prefrontal, insular and anterior cingulate regions. This work suggests that, in early infancy, functional integration through thalamocortical connections depends on significant functional overlap in the topographic organization of the thalamus and that the experience of premature extrauterine life modulates network development, altering the maturation of networks thought to support salience, executive, integrative, and cognitive functions. PMID:25941391

Long-Term Functional Outcomes and Correlation with Regional Brain Connectivity by MRI Diffusion Tractography Metrics in a Near-Term Rabbit Model of Intrauterine Growth Restriction

PubMed Central

Illa, Miriam; Eixarch, Elisenda; Batalle, Dafnis; Arbat-Plana, Ariadna; Muñoz-Moreno, Emma; Figueras, Francesc; Gratacos, Eduard

2013-01-01

Background Intrauterine growth restriction (IUGR) affects 5–10% of all newborns and is associated with increased risk of memory, attention and anxiety problems in late childhood and adolescence. The neurostructural correlates of long-term abnormal neurodevelopment associated with IUGR are unknown. Thus, the aim of this study was to provide a comprehensive description of the long-term functional and neurostructural correlates of abnormal neurodevelopment associated with IUGR in a near-term rabbit model (delivered at 30 days of gestation) and evaluate the development of quantitative imaging biomarkers of abnormal neurodevelopment based on diffusion magnetic resonance imaging (MRI) parameters and connectivity. Methodology At +70 postnatal days, 10 cases and 11 controls were functionally evaluated with the Open Field Behavioral Test which evaluates anxiety and attention and the Object Recognition Task that evaluates short-term memory and attention. Subsequently, brains were collected, fixed and a high resolution MRI was performed. Differences in diffusion parameters were analyzed by means of voxel-based and connectivity analysis measuring the number of fibers reconstructed within anxiety, attention and short-term memory networks over the total fibers. Principal Findings The results of the neurobehavioral and cognitive assessment showed a significant higher degree of anxiety, attention and memory problems in cases compared to controls in most of the variables explored. Voxel-based analysis (VBA) revealed significant differences between groups in multiple brain regions mainly in grey matter structures, whereas connectivity analysis demonstrated lower ratios of fibers within the networks in cases, reaching the statistical significance only in the left hemisphere for both networks. Finally, VBA and connectivity results were also correlated with functional outcome. Conclusions The rabbit model used reproduced long-term functional impairments and their neurostructural correlates of abnormal neurodevelopment associated with IUGR. The description of the pattern of microstructural changes underlying functional defects may help to develop biomarkers based in diffusion MRI and connectivity analysis. PMID:24143189

Long-term functional outcomes and correlation with regional brain connectivity by MRI diffusion tractography metrics in a near-term rabbit model of intrauterine growth restriction.

PubMed

Illa, Miriam; Eixarch, Elisenda; Batalle, Dafnis; Arbat-Plana, Ariadna; Muñoz-Moreno, Emma; Figueras, Francesc; Gratacos, Eduard

2013-01-01

Intrauterine growth restriction (IUGR) affects 5-10% of all newborns and is associated with increased risk of memory, attention and anxiety problems in late childhood and adolescence. The neurostructural correlates of long-term abnormal neurodevelopment associated with IUGR are unknown. Thus, the aim of this study was to provide a comprehensive description of the long-term functional and neurostructural correlates of abnormal neurodevelopment associated with IUGR in a near-term rabbit model (delivered at 30 days of gestation) and evaluate the development of quantitative imaging biomarkers of abnormal neurodevelopment based on diffusion magnetic resonance imaging (MRI) parameters and connectivity. At +70 postnatal days, 10 cases and 11 controls were functionally evaluated with the Open Field Behavioral Test which evaluates anxiety and attention and the Object Recognition Task that evaluates short-term memory and attention. Subsequently, brains were collected, fixed and a high resolution MRI was performed. Differences in diffusion parameters were analyzed by means of voxel-based and connectivity analysis measuring the number of fibers reconstructed within anxiety, attention and short-term memory networks over the total fibers. The results of the neurobehavioral and cognitive assessment showed a significant higher degree of anxiety, attention and memory problems in cases compared to controls in most of the variables explored. Voxel-based analysis (VBA) revealed significant differences between groups in multiple brain regions mainly in grey matter structures, whereas connectivity analysis demonstrated lower ratios of fibers within the networks in cases, reaching the statistical significance only in the left hemisphere for both networks. Finally, VBA and connectivity results were also correlated with functional outcome. The rabbit model used reproduced long-term functional impairments and their neurostructural correlates of abnormal neurodevelopment associated with IUGR. The description of the pattern of microstructural changes underlying functional defects may help to develop biomarkers based in diffusion MRI and connectivity analysis.

Closed-loop, pilot/vehicle analysis of the approach and landing task

NASA Technical Reports Server (NTRS)

Anderson, M. R.; Schmidt, D. K.

1986-01-01

In the case of approach and landing, it is universally accepted that the pilot uses more than one vehicle response, or output, to close his control loops. Therefore, to model this task, a multi-loop analysis technique is required. The analysis problem has been in obtaining reasonable analytic estimates of the describing functions representing the pilot's loop compensation. Once these pilot describing functions are obtained, appropriate performance and workload metrics must then be developed for the landing task. The optimal control approach provides a powerful technique for obtaining the necessary describing functions, once the appropriate task objective is defined in terms of a quadratic objective function. An approach is presented through the use of a simple, reasonable objective function and model-based metrics to evaluate loop performance and pilot workload. The results of an analysis of the LAHOS (Landing and Approach of Higher Order Systems) study performed by R.E. Smith is also presented.

Functional Behavioral Assessment-Based Interventions for Students with or at Risk for Emotional and/or Behavioral Disorders in School: A Hierarchical Linear Modeling Meta-Analysis

ERIC Educational Resources Information Center

Gage, Nicholas A.; Lewis, Timothy J.; Stichter, Janine P.

2012-01-01

Of the myriad practices currently utilized for students with disabilities, particularly students with or at risk for emotional and/or behavioral disorder (EBD), functional behavior assessment (FBA) is a practice with an emerging solid research base. However, the FBA research base relies on single-subject design (SSD) and synthesis has relied on…

GeoPAT: A toolbox for pattern-based information retrieval from large geospatial databases

NASA Astrophysics Data System (ADS)

Jasiewicz, Jarosław; Netzel, Paweł; Stepinski, Tomasz

2015-07-01

Geospatial Pattern Analysis Toolbox (GeoPAT) is a collection of GRASS GIS modules for carrying out pattern-based geospatial analysis of images and other spatial datasets. The need for pattern-based analysis arises when images/rasters contain rich spatial information either because of their very high resolution or their very large spatial extent. Elementary units of pattern-based analysis are scenes - patches of surface consisting of a complex arrangement of individual pixels (patterns). GeoPAT modules implement popular GIS algorithms, such as query, overlay, and segmentation, to operate on the grid of scenes. To achieve these capabilities GeoPAT includes a library of scene signatures - compact numerical descriptors of patterns, and a library of distance functions - providing numerical means of assessing dissimilarity between scenes. Ancillary GeoPAT modules use these functions to construct a grid of scenes or to assign signatures to individual scenes having regular or irregular geometries. Thus GeoPAT combines knowledge retrieval from patterns with mapping tasks within a single integrated GIS environment. GeoPAT is designed to identify and analyze complex, highly generalized classes in spatial datasets. Examples include distinguishing between different styles of urban settlements using VHR images, delineating different landscape types in land cover maps, and mapping physiographic units from DEM. The concept of pattern-based spatial analysis is explained and the roles of all modules and functions are described. A case study example pertaining to delineation of landscape types in a subregion of NLCD is given. Performance evaluation is included to highlight GeoPAT's applicability to very large datasets. The GeoPAT toolbox is available for download from

Functional network connectivity analysis based on partial correlation in Alzheimer's disease

NASA Astrophysics Data System (ADS)

Zhang, Nan; Guan, Xiaoting; Zhang, Yumei; Li, Jingjing; Chen, Hongyan; Chen, Kewei; Fleisher, Adam; Yao, Li; Wu, Xia

2009-02-01

Functional network connectivity (FNC) measures the temporal dependency among the time courses of functional networks. However, the marginal correlation between two networks used in the classic FNC analysis approach doesn't separate the FNC from the direct/indirect effects of other networks. In this study, we proposed an alternative approach based on partial correlation to evaluate the FNC, since partial correlation based FNC can reveal the direct interaction between a pair of networks, removing dependencies or influences from others. Previous studies have demonstrated less task-specific activation and less rest-state activity in Alzheimer's disease (AD). We applied present approach to contrast FNC differences of resting state network (RSN) between AD and normal controls (NC). The fMRI data under resting condition were collected from 15 AD and 16 NC. FNC was calculated for each pair of six RSNs identified using Group ICA, thus resulting in 15 (2 out of 6) pairs for each subject. Partial correlation based FNC analysis indicated 6 pairs significant differences between groups, while marginal correlation only revealed 2 pairs (involved in the partial correlation results). Additionally, patients showed lower correlation than controls among most of the FNC differences. Our results provide new evidences for the disconnection hypothesis in AD.

Joint multifractal analysis based on the partition function approach: analytical analysis, numerical simulation and empirical application

NASA Astrophysics Data System (ADS)

Xie, Wen-Jie; Jiang, Zhi-Qiang; Gu, Gao-Feng; Xiong, Xiong; Zhou, Wei-Xing

2015-10-01

Many complex systems generate multifractal time series which are long-range cross-correlated. Numerous methods have been proposed to characterize the multifractal nature of these long-range cross correlations. However, several important issues about these methods are not well understood and most methods consider only one moment order. We study the joint multifractal analysis based on partition function with two moment orders, which was initially invented to investigate fluid fields, and derive analytically several important properties. We apply the method numerically to binomial measures with multifractal cross correlations and bivariate fractional Brownian motions without multifractal cross correlations. For binomial multifractal measures, the explicit expressions of mass function, singularity strength and multifractal spectrum of the cross correlations are derived, which agree excellently with the numerical results. We also apply the method to stock market indexes and unveil intriguing multifractality in the cross correlations of index volatilities.

Key Microbiota Identification Using Functional Gene Analysis during Pepper (Piper nigrum L.) Peeling.

PubMed

Zhang, Jiachao; Hu, Qisong; Xu, Chuanbiao; Liu, Sixin; Li, Congfa

2016-01-01

Pepper pericarp microbiota plays an important role in the pepper peeling process for the production of white pepper. We collected pepper samples at different peeling time points from Hainan Province, China, and used a metagenomic approach to identify changes in the pericarp microbiota based on functional gene analysis. UniFrac distance-based principal coordinates analysis revealed significant changes in the pericarp microbiota structure during peeling, which were attributed to increases in bacteria from the genera Selenomonas and Prevotella. We identified 28 core operational taxonomic units at each time point, mainly belonging to Selenomonas, Prevotella, Megasphaera, Anaerovibrio, and Clostridium genera. The results were confirmed by quantitative polymerase chain reaction. At the functional level, we observed significant increases in microbial features related to acetyl xylan esterase and pectinesterase for pericarp degradation during peeling. These findings offer a new insight into biodegradation for pepper peeling and will promote the development of the white pepper industry.

Key Microbiota Identification Using Functional Gene Analysis during Pepper (Piper nigrum L.) Peeling

PubMed Central

Xu, Chuanbiao; Liu, Sixin; Li, Congfa

2016-01-01

Pepper pericarp microbiota plays an important role in the pepper peeling process for the production of white pepper. We collected pepper samples at different peeling time points from Hainan Province, China, and used a metagenomic approach to identify changes in the pericarp microbiota based on functional gene analysis. UniFrac distance-based principal coordinates analysis revealed significant changes in the pericarp microbiota structure during peeling, which were attributed to increases in bacteria from the genera Selenomonas and Prevotella. We identified 28 core operational taxonomic units at each time point, mainly belonging to Selenomonas, Prevotella, Megasphaera, Anaerovibrio, and Clostridium genera. The results were confirmed by quantitative polymerase chain reaction. At the functional level, we observed significant increases in microbial features related to acetyl xylan esterase and pectinesterase for pericarp degradation during peeling. These findings offer a new insight into biodegradation for pepper peeling and will promote the development of the white pepper industry. PMID:27768750

In-vehicle signing concepts: An analytical precursor to an in-vehicle information system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Spelt, P.F.; Tufano, D.R.; Knee, H.E.

The purpose of the project described in this report is to develop alternative In-Vehicle Signing (IVS) system concepts based on allocation of the functions associated with driving a road vehicle. In the driving milieu, tasks can be assigned to one of three agents, the driver, the vehicle or the infrastructure. Assignment of tasks is based on a philosophy of function allocation which can emphasize any of several philosophical approaches. In this project, function allocations were made according to the current practice in vehicle design and signage as well as a human-centered strategy. Several IVS system concepts are presented based onmore » differing functional allocation outcomes. A design space for IVS systems is described, and a technical analysis of a map-based and sever beacon-based IVS systems are presented. Because of problems associated with both map-based and beacon-based concepts, a hybrid IVS concept was proposed. The hybrid system uses on-board map-based databases to serve those areas in which signage can be anticipated to be relatively static, such as large metropolitan areas where few if any new roads will be built. For areas where sign density is low, and/or where population growth causes changes in traffic flow, beacon-based concepts function best. For this situation, changes need only occur in the central database from which sign information is transmitted. This report presents system concepts which enable progress from the IVS system concept-independent functional requirements to a more specific set of system concepts which facilitate analysis and selection of hardware and software to perform the functions of IVS. As such, this phase of the project represents a major step toward the design and development of a prototype WS system. Once such a system is developed, a program of testing, evaluation, an revision will be undertaken. Ultimately, such a system can become part of the road vehicle of the future.« less

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights

PubMed Central

Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-01

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher’s exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO’s usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher. PMID:26750448

LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.

PubMed

Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

2016-01-11

Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.

An improved method for functional similarity analysis of genes based on Gene Ontology.

PubMed

Tian, Zhen; Wang, Chunyu; Guo, Maozu; Liu, Xiaoyan; Teng, Zhixia

2016-12-23

Measures of gene functional similarity are essential tools for gene clustering, gene function prediction, evaluation of protein-protein interaction, disease gene prioritization and other applications. In recent years, many gene functional similarity methods have been proposed based on the semantic similarity of GO terms. However, these leading approaches may make errorprone judgments especially when they measure the specificity of GO terms as well as the IC of a term set. Therefore, how to estimate the gene functional similarity reliably is still a challenging problem. We propose WIS, an effective method to measure the gene functional similarity. First of all, WIS computes the IC of a term by employing its depth, the number of its ancestors as well as the topology of its descendants in the GO graph. Secondly, WIS calculates the IC of a term set by means of considering the weighted inherited semantics of terms. Finally, WIS estimates the gene functional similarity based on the IC overlap ratio of term sets. WIS is superior to some other representative measures on the experiments of functional classification of genes in a biological pathway, collaborative evaluation of GO-based semantic similarity measures, protein-protein interaction prediction and correlation with gene expression. Further analysis suggests that WIS takes fully into account the specificity of terms and the weighted inherited semantics of terms between GO terms. The proposed WIS method is an effective and reliable way to compare gene function. The web service of WIS is freely available at http://nclab.hit.edu.cn/WIS/ .

Intrinsic Resting-State Functional Connectivity in the Human Spinal Cord at 3.0 T.

PubMed

San Emeterio Nateras, Oscar; Yu, Fang; Muir, Eric R; Bazan, Carlos; Franklin, Crystal G; Li, Wei; Li, Jinqi; Lancaster, Jack L; Duong, Timothy Q

2016-04-01

To apply resting-state functional magnetic resonance (MR) imaging to map functional connectivity of the human spinal cord. Studies were performed in nine self-declared healthy volunteers with informed consent and institutional review board approval. Resting-state functional MR imaging was performed to map functional connectivity of the human cervical spinal cord from C1 to C4 at 1 × 1 × 3-mm resolution with a 3.0-T clinical MR imaging unit. Independent component analysis (ICA) was performed to derive resting-state functional MR imaging z-score maps rendered on two-dimensional and three-dimensional images. Seed-based analysis was performed for cross validation with ICA networks by using Pearson correlation. Reproducibility analysis of resting-state functional MR imaging maps from four repeated trials in a single participant yielded a mean z score of 6 ± 1 (P < .0001). The centroid coordinates across the four trials deviated by 2 in-plane voxels ± 2 mm (standard deviation) and up to one adjacent image section ± 3 mm. ICA of group resting-state functional MR imaging data revealed prominent functional connectivity patterns within the spinal cord gray matter. There were statistically significant (z score > 3, P < .001) bilateral, unilateral, and intersegmental correlations in the ventral horns, dorsal horns, and central spinal cord gray matter. Three-dimensional surface rendering provided visualization of these components along the length of the spinal cord. Seed-based analysis showed that many ICA components exhibited strong and significant (P < .05) correlations, corroborating the ICA results. Resting-state functional MR imaging connectivity networks are qualitatively consistent with known neuroanatomic and functional structures in the spinal cord. Resting-state functional MR imaging of the human cervical spinal cord with a 3.0-T clinical MR imaging unit and standard MR imaging protocols and hardware reveals prominent functional connectivity patterns within the spinal cord gray matter, consistent with known functional and anatomic layouts of the spinal cord.

A Bifactor Multidimensional Item Response Theory Model for Differential Item Functioning Analysis on Testlet-Based Items

ERIC Educational Resources Information Center

Fukuhara, Hirotaka; Kamata, Akihito

2011-01-01

A differential item functioning (DIF) detection method for testlet-based data was proposed and evaluated in this study. The proposed DIF model is an extension of a bifactor multidimensional item response theory (MIRT) model for testlets. Unlike traditional item response theory (IRT) DIF models, the proposed model takes testlet effects into…

An Introduction to Differentials Based on Hyperreal Numbers and Infinite Microscopes

ERIC Educational Resources Information Center

Henry, Valerie

2010-01-01

In this article, we propose to introduce the differential of a function through a non-classical way, lying on hyperreals and infinite microscopes. This approach is based on the developments of nonstandard analysis, wants to be more intuitive than the classical one and tries to emphasize the functional and geometric aspects of the differential. In…

Teacher Implementation of Trial-Based Functional Analysis and Function-Based Interventions for Students with Challenging Behavior

ERIC Educational Resources Information Center

Flynn, Susan Dagenhart

2012-01-01

Children and youth with autism spectrum disorders (ASD) or emotional and behavioral disabilities (E/BD) often exhibit challenging behavior including aggression, self-injury, non-compliance, or property destruction (Kamps, Kravits, Rauch, Kamps, & Chung, 2000; National Autism Center, 2009). As a result, students with ASD or E/BD often miss out…

Comparing rainfall patterns between regions in Peninsular Malaysia via a functional data analysis technique

NASA Astrophysics Data System (ADS)

Suhaila, Jamaludin; Jemain, Abdul Aziz; Hamdan, Muhammad Fauzee; Wan Zin, Wan Zawiah

2011-12-01

SummaryNormally, rainfall data is collected on a daily, monthly or annual basis in the form of discrete observations. The aim of this study is to convert these rainfall values into a smooth curve or function which could be used to represent the continuous rainfall process at each region via a technique known as functional data analysis. Since rainfall data shows a periodic pattern in each region, the Fourier basis is introduced to capture these variations. Eleven basis functions with five harmonics are used to describe the unimodal rainfall pattern for stations in the East while five basis functions which represent two harmonics are needed to describe the rainfall pattern in the West. Based on the fitted smooth curve, the wet and dry periods as well as the maximum and minimum rainfall values could be determined. Different rainfall patterns are observed among the studied regions based on the smooth curve. Using the functional analysis of variance, the test results indicated that there exist significant differences in the functional means between each region. The largest differences in the functional means are found between the East and Northwest regions and these differences may probably be due to the effect of topography and, geographical location and are mostly influenced by the monsoons. Therefore, the same inputs or approaches might not be useful in modeling the hydrological process for different regions.

Effects of Aquatic Therapy and Land-Based Therapy versus Land-Based Therapy Alone on Range of Motion, Edema, and Function after Hip or Knee Replacement: A Systematic Review and Meta-analysis

PubMed Central

Shields, Nora

2015-01-01

ABSTRACT Purpose: To determine whether aquatic therapy in combination with land-based therapy improves patient outcomes after hip or knee arthroplasty compared with land-based therapy alone. Methods: For this systematic review, six online databases (MEDLINE, CINAHL, AMED, EMBASE, Cochrane, and PEDro) were searched from the earliest date available until September 2013. Controlled trials published in English in a peer-reviewed journal that compared aquatic therapy in combination with land-based therapy with land-based therapy alone were included; trial quality was assessed using the PEDro scale. Data were presented as standardized mean differences (SMDs), their associated 95% CIs, and meta-analyses. Results: Three small trials of moderate quality were included in the qualitative analysis. Meta-analysis of two of these studies found moderate-quality evidence that aquatic therapy in combination with land-based therapy improves functional outcomes (SMD=0.53; 95% CI, 0.03–1.03), knee range of motion (measured in knee or hip arthroplasty; SMD=0.78; 95% CI, 0.27–1.29), and edema (SMD=−0.66; 95% CI, −1.16 to −0.15) compared with land-based therapy alone. The results for improved functional outcomes were not considered clinically significant. Conclusions: It is not possible to draw confident conclusions from this review because of the small number of studies of limited quality and the modest differences found. Further studies of sound methodological quality are required to confirm the results. Economic analysis alongside randomized controlled trials is needed to examine the cost-effectiveness of these clinical outcomes. PMID:25931664

Response surface method in geotechnical/structural analysis, phase 1

NASA Astrophysics Data System (ADS)

Wong, F. S.

1981-02-01

In the response surface approach, an approximating function is fit to a long running computer code based on a limited number of code calculations. The approximating function, called the response surface, is then used to replace the code in subsequent repetitive computations required in a statistical analysis. The procedure of the response surface development and feasibility of the method are shown using a sample problem in slop stability which is based on data from centrifuge experiments of model soil slopes and involves five random soil parameters. It is shown that a response surface can be constructed based on as few as four code calculations and that the response surface is computationally extremely efficient compared to the code calculation. Potential applications of this research include probabilistic analysis of dynamic, complex, nonlinear soil/structure systems such as slope stability, liquefaction, and nuclear reactor safety.

Comparison of Traditional and Trial-Based Methodologies for Conducting Functional Analyses

ERIC Educational Resources Information Center

LaRue, Robert H.; Lenard, Karen; Weiss, Mary Jane; Bamond, Meredith; Palmieri, Mark; Kelley, Michael E.

2010-01-01

Functional analysis represents a sophisticated and empirically supported functional assessment procedure. While these procedures have garnered considerable empirical support, they are often underused in clinical practice. Safety risks resulting from the evocation of maladaptive behavior and the length of time required to conduct functional…

Discovering transnosological molecular basis of human brain diseases using biclustering analysis of integrated gene expression data.

PubMed

Cha, Kihoon; Hwang, Taeho; Oh, Kimin; Yi, Gwan-Su

2015-01-01

It has been reported that several brain diseases can be treated as transnosological manner implicating possible common molecular basis under those diseases. However, molecular level commonality among those brain diseases has been largely unexplored. Gene expression analyses of human brain have been used to find genes associated with brain diseases but most of those studies were restricted either to an individual disease or to a couple of diseases. In addition, identifying significant genes in such brain diseases mostly failed when it used typical methods depending on differentially expressed genes. In this study, we used a correlation-based biclustering approach to find coexpressed gene sets in five neurodegenerative diseases and three psychiatric disorders. By using biclustering analysis, we could efficiently and fairly identified various gene sets expressed specifically in both single and multiple brain diseases. We could find 4,307 gene sets correlatively expressed in multiple brain diseases and 3,409 gene sets exclusively specified in individual brain diseases. The function enrichment analysis of those gene sets showed many new possible functional bases as well as neurological processes that are common or specific for those eight diseases. This study introduces possible common molecular bases for several brain diseases, which open the opportunity to clarify the transnosological perspective assumed in brain diseases. It also showed the advantages of correlation-based biclustering analysis and accompanying function enrichment analysis for gene expression data in this type of investigation.

Discovering transnosological molecular basis of human brain diseases using biclustering analysis of integrated gene expression data

PubMed Central

2015-01-01

Background It has been reported that several brain diseases can be treated as transnosological manner implicating possible common molecular basis under those diseases. However, molecular level commonality among those brain diseases has been largely unexplored. Gene expression analyses of human brain have been used to find genes associated with brain diseases but most of those studies were restricted either to an individual disease or to a couple of diseases. In addition, identifying significant genes in such brain diseases mostly failed when it used typical methods depending on differentially expressed genes. Results In this study, we used a correlation-based biclustering approach to find coexpressed gene sets in five neurodegenerative diseases and three psychiatric disorders. By using biclustering analysis, we could efficiently and fairly identified various gene sets expressed specifically in both single and multiple brain diseases. We could find 4,307 gene sets correlatively expressed in multiple brain diseases and 3,409 gene sets exclusively specified in individual brain diseases. The function enrichment analysis of those gene sets showed many new possible functional bases as well as neurological processes that are common or specific for those eight diseases. Conclusions This study introduces possible common molecular bases for several brain diseases, which open the opportunity to clarify the transnosological perspective assumed in brain diseases. It also showed the advantages of correlation-based biclustering analysis and accompanying function enrichment analysis for gene expression data in this type of investigation. PMID:26043779

Daily home-based spirometry during withdrawal of inhaled corticosteroid in severe to very severe chronic obstructive pulmonary disease

PubMed Central

Rodriguez-Roisin, Roberto; Tetzlaff, Kay; Watz, Henrik; Wouters, Emiel FM; Disse, Bernd; Finnigan, Helen; Magnussen, Helgo; Calverley, Peter MA

2016-01-01

The WISDOM study (NCT00975195) reported a change in lung function following withdrawal of fluticasone propionate in patients with severe to very severe COPD treated with tiotropium and salmeterol. However, little is known about the validity of home-based spirometry measurements of lung function in COPD. Therefore, as part of this study, following suitable training, patients recorded daily home-based spirometry measurements in addition to undergoing periodic in-clinic spirometric testing throughout the study duration. We subsequently determined the validity of home-based spirometry for detecting changes in lung function by comparing in-clinic and home-based forced expiratory volume in 1 second in patients who underwent stepwise fluticasone propionate withdrawal over 12 weeks versus patients remaining on fluticasone propionate for 52 weeks. Bland–Altman analysis of these data confirmed good agreement between in-clinic and home-based measurements, both across all visits and at the individual visits at study weeks 6, 12, 18, and 52. There was a measurable difference between the forced expiratory volume in 1 second values recorded at home and in the clinic (mean difference of −0.05 L), which may be due to suboptimal patient effort in performing unsupervised recordings. However, this difference remained consistent over time. Overall, these data demonstrate that home-based and in-clinic spirometric measurements were equally valid and reliable for assessing lung function in patients with COPD, and suggest that home-based spirometry may be a useful tool to facilitate analysis of changes in lung function on a day-to-day basis. PMID:27578972

Risk Based Reliability Centered Maintenance of DOD Fire Protection Systems

DTIC Science & Technology

1999-01-01

2.2.3 Failure Mode and Effect Analysis ( FMEA )............................ 2.2.4 Failure Mode Risk Characterization...Step 2 - System functions and functional failures definition Step 3 - Failure mode and effect analysis ( FMEA ) Step 4 - Failure mode risk...system). The Interface Location column identifies the location where the FMEA of the fire protection system began or stopped. For example, for the fire

Gradient descent for robust kernel-based regression

NASA Astrophysics Data System (ADS)

Guo, Zheng-Chu; Hu, Ting; Shi, Lei

2018-06-01

In this paper, we study the gradient descent algorithm generated by a robust loss function over a reproducing kernel Hilbert space (RKHS). The loss function is defined by a windowing function G and a scale parameter σ, which can include a wide range of commonly used robust losses for regression. There is still a gap between theoretical analysis and optimization process of empirical risk minimization based on loss: the estimator needs to be global optimal in the theoretical analysis while the optimization method can not ensure the global optimality of its solutions. In this paper, we aim to fill this gap by developing a novel theoretical analysis on the performance of estimators generated by the gradient descent algorithm. We demonstrate that with an appropriately chosen scale parameter σ, the gradient update with early stopping rules can approximate the regression function. Our elegant error analysis can lead to convergence in the standard L 2 norm and the strong RKHS norm, both of which are optimal in the mini-max sense. We show that the scale parameter σ plays an important role in providing robustness as well as fast convergence. The numerical experiments implemented on synthetic examples and real data set also support our theoretical results.

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Lacey, Simon; Sathian, K

2018-02-01

In a recent study Eklund et al. have shown that cluster-wise family-wise error (FWE) rate-corrected inferences made in parametric statistical method-based functional magnetic resonance imaging (fMRI) studies over the past couple of decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; principally because the spatial autocorrelation functions (sACFs) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggest otherwise. Hence, the residuals from general linear model (GLM)-based fMRI activation estimates in these studies may not have possessed a homogenously Gaussian sACF. Here we propose a method based on the assumption that heterogeneity and non-Gaussianity of the sACF of the first-level GLM analysis residuals, as well as temporal autocorrelations in the first-level voxel residual time-series, are caused by unmodeled MRI signal from neuronal and physiological processes as well as motion and other artifacts, which can be approximated by appropriate decompositions of the first-level residuals with principal component analysis (PCA), and removed. We show that application of this method yields GLM residuals with significantly reduced spatial correlation, nearly Gaussian sACF and uniform spatial smoothness across the brain, thereby allowing valid cluster-based FWE-corrected inferences based on assumption of Gaussian spatial noise. We further show that application of this method renders the voxel time-series of first-level GLM residuals independent, and identically distributed across time (which is a necessary condition for appropriate voxel-level GLM inference), without having to fit ad hoc stochastic colored noise models. Furthermore, the detection power of individual subject brain activation analysis is enhanced. This method will be especially useful for case studies, which rely on first-level GLM analysis inferences.

Students creative thinking skills in solving two dimensional arithmetic series through research-based learning

NASA Astrophysics Data System (ADS)

Tohir, M.; Abidin, Z.; Dafik; Hobri

2018-04-01

Arithmetics is one of the topics in Mathematics, which deals with logic and detailed process upon generalizing formula. Creativity and flexibility are needed in generalizing formula of arithmetics series. This research aimed at analyzing students creative thinking skills in generalizing arithmetic series. The triangulation method and research-based learning was used in this research. The subjects were students of the Master Program of Mathematics Education in Faculty of Teacher Training and Education at Jember University. The data was collected by giving assignments to the students. The data collection was done by giving open problem-solving task and documentation study to the students to arrange generalization pattern based on the dependent function formula i and the function depend on i and j. Then, the students finished the next problem-solving task to construct arithmetic generalization patterns based on the function formula which depends on i and i + n and the sum formula of functions dependent on i and j of the arithmetic compiled. The data analysis techniques operative in this study was Miles and Huberman analysis model. Based on the result of data analysis on task 1, the levels of students creative thinking skill were classified as follows; 22,22% of the students categorized as “not creative” 38.89% of the students categorized as “less creative” category; 22.22% of the students categorized as “sufficiently creative” and 16.67% of the students categorized as “creative”. By contrast, the results of data analysis on task 2 found that the levels of students creative thinking skills were classified as follows; 22.22% of the students categorized as “sufficiently creative”, 44.44% of the students categorized as “creative” and 33.33% of the students categorized as “very creative”. This analysis result can set the basis for teaching references and actualizing a better teaching model in order to increase students creative thinking skills.

Neuronal Correlates of Individual Differences in the Big Five Personality Traits: Evidences from Cortical Morphology and Functional Homogeneity.

PubMed

Li, Ting; Yan, Xu; Li, Yuan; Wang, Junjie; Li, Qiang; Li, Hong; Li, Junfeng

2017-01-01

There have been many neuroimaging studies of human personality traits, and it have already provided glimpse into the neurobiology of complex traits. And most of previous studies adopt voxel-based morphology (VBM) analysis to explore the brain-personality mechanism from two levels (vertex and regional based), the findings are mixed with great inconsistencies and the brain-personality relations are far from a full understanding. Here, we used a new method of surface-based morphology (SBM) analysis, which provides better alignment of cortical landmarks to generate about the associations between cortical morphology and the personality traits across 120 healthy individuals at both vertex and regional levels. While to further reveal local functional correlates of the morphology-personality relationships, we related surface-based functional homogeneity measures to the regions identified in the regional-based SBM correlation. Vertex-wise analysis revealed that people with high agreeableness exhibited larger areas in the left superior temporal gyrus. Based on regional parcellation we found that extroversion was negatively related with the volume of the left lateral occipito-temporal gyrus and agreeableness was negatively associated with the sulcus depth of the left superior parietal lobule. Moreover, increased regional homogeneity in the left lateral occipito-temporal gyrus is related to the scores of extroversion, and increased regional homogeneity in the left superior parietal lobule is related to the scores of agreeableness. These findings provide supporting evidence of a link between personality and brain structural mysteries with a method of SBM, and further suggest that local functional homogeneity of personality traits has neurobiological relevance that is likely based on anatomical substrates.

Resting-state theta band connectivity and graph analysis in generalized social anxiety disorder.

PubMed

Xing, Mengqi; Tadayonnejad, Reza; MacNamara, Annmarie; Ajilore, Olusola; DiGangi, Julia; Phan, K Luan; Leow, Alex; Klumpp, Heide

2017-01-01

Functional magnetic resonance imaging (fMRI) resting-state studies show generalized social anxiety disorder (gSAD) is associated with disturbances in networks involved in emotion regulation, emotion processing, and perceptual functions, suggesting a network framework is integral to elucidating the pathophysiology of gSAD. However, fMRI does not measure the fast dynamic interconnections of functional networks. Therefore, we examined whole-brain functional connectomics with electroencephalogram (EEG) during resting-state. Resting-state EEG data was recorded for 32 patients with gSAD and 32 demographically-matched healthy controls (HC). Sensor-level connectivity analysis was applied on EEG data by using Weighted Phase Lag Index (WPLI) and graph analysis based on WPLI was used to determine clustering coefficient and characteristic path length to estimate local integration and global segregation of networks. WPLI results showed increased oscillatory midline coherence in the theta frequency band indicating higher connectivity in the gSAD relative to HC group during rest. Additionally, WPLI values positively correlated with state anxiety levels within the gSAD group but not the HC group. Our graph theory based connectomics analysis demonstrated increased clustering coefficient and decreased characteristic path length in theta-based whole brain functional organization in subjects with gSAD compared to HC. Theta-dependent interconnectivity was associated with state anxiety in gSAD and an increase in information processing efficiency in gSAD (compared to controls). Results may represent enhanced baseline self-focused attention, which is consistent with cognitive models of gSAD and fMRI studies implicating emotion dysregulation and disturbances in task negative networks (e.g., default mode network) in gSAD.

Proximity-Based Differential Single-Cell Analysis of the Niche to Identify Stem/Progenitor Cell Regulators.

PubMed

Silberstein, Lev; Goncalves, Kevin A; Kharchenko, Peter V; Turcotte, Raphael; Kfoury, Youmna; Mercier, Francois; Baryawno, Ninib; Severe, Nicolas; Bachand, Jacqueline; Spencer, Joel A; Papazian, Ani; Lee, Dongjun; Chitteti, Brahmananda Reddy; Srour, Edward F; Hoggatt, Jonathan; Tate, Tiffany; Lo Celso, Cristina; Ono, Noriaki; Nutt, Stephen; Heino, Jyrki; Sipilä, Kalle; Shioda, Toshihiro; Osawa, Masatake; Lin, Charles P; Hu, Guo-Fu; Scadden, David T

2016-10-06

Physiological stem cell function is regulated by secreted factors produced by niche cells. In this study, we describe an unbiased approach based on the differential single-cell gene expression analysis of mesenchymal osteolineage cells close to, and further removed from, hematopoietic stem/progenitor cells (HSPCs) to identify candidate niche factors. Mesenchymal cells displayed distinct molecular profiles based on their relative location. We functionally examined, among the genes that were preferentially expressed in proximal cells, three secreted or cell-surface molecules not previously connected to HSPC biology-the secreted RNase angiogenin, the cytokine IL18, and the adhesion molecule Embigin-and discovered that all of these factors are HSPC quiescence regulators. Therefore, our proximity-based differential single-cell approach reveals molecular heterogeneity within niche cells and can be used to identify novel extrinsic stem/progenitor cell regulators. Similar approaches could also be applied to other stem cell/niche pairs to advance the understanding of microenvironmental regulation of stem cell function. Copyright © 2016 Elsevier Inc. All rights reserved.

Transfection microarray and the applications.

PubMed

Miyake, Masato; Yoshikawa, Tomohiro; Fujita, Satoshi; Miyake, Jun

2009-05-01

Microarray transfection has been extensively studied for high-throughput functional analysis of mammalian cells. However, control of efficiency and reproducibility are the critical issues for practical use. By using solid-phase transfection accelerators and nano-scaffold, we provide a highly efficient and reproducible microarray-transfection device, "transfection microarray". The device would be applied to the limited number of available primary cells and stem cells not only for large-scale functional analysis but also reporter-based time-lapse cellular event analysis.

Assessment of heart rate variability based on mobile device for planning physical activity

NASA Astrophysics Data System (ADS)

Svirin, I. S.; Epishina, E. V.; Voronin, V. V.; Semenishchev, E. A.; Solodova, E. N.; Nabilskaya, N. V.

2015-05-01

In this paper we present a method for the functional analysis of human heart based on electrocardiography (ECG) signals. The approach using the apparatus of analytical and differential geometry and correlation and regression analysis. ECG contains information on the current condition of the cardiovascular system as well as on the pathological changes in the heart. Mathematical processing of the heart rate variability allows to obtain a great set of mathematical and statistical characteristics. These characteristics of the heart rate are used when solving research problems to study physiological changes that determine functional changes of an individual. The proposed method implemented for up-to-date mobile Android and iOS based devices.

Using EIGER for Antenna Design and Analysis

NASA Technical Reports Server (NTRS)

Champagne, Nathan J.; Khayat, Michael; Kennedy, Timothy F.; Fink, Patrick W.

2007-01-01

EIGER (Electromagnetic Interactions GenERalized) is a frequency-domain electromagnetics software package that is built upon a flexible framework, designed using object-oriented techniques. The analysis methods used include moment method solutions of integral equations, finite element solutions of partial differential equations, and combinations thereof. The framework design permits new analysis techniques (boundary conditions, Green#s functions, etc.) to be added to the software suite with a sensible effort. The code has been designed to execute (in serial or parallel) on a wide variety of platforms from Intel-based PCs and Unix-based workstations. Recently, new potential integration scheme s that avoid singularity extraction techniques have been added for integral equation analysis. These new integration schemes are required for facilitating the use of higher-order elements and basis functions. Higher-order elements are better able to model geometrical curvature using fewer elements than when using linear elements. Higher-order basis functions are beneficial for simulating structures with rapidly varying fields or currents. Results presented here will demonstrate curren t and future capabilities of EIGER with respect to analysis of installed antenna system performance in support of NASA#s mission of exploration. Examples include antenna coupling within an enclosed environment and antenna analysis on electrically large manned space vehicles.

Aerodynamic design optimization using sensitivity analysis and computational fluid dynamics

NASA Technical Reports Server (NTRS)

Baysal, Oktay; Eleshaky, Mohamed E.

1991-01-01

A new and efficient method is presented for aerodynamic design optimization, which is based on a computational fluid dynamics (CFD)-sensitivity analysis algorithm. The method is applied to design a scramjet-afterbody configuration for an optimized axial thrust. The Euler equations are solved for the inviscid analysis of the flow, which in turn provides the objective function and the constraints. The CFD analysis is then coupled with the optimization procedure that uses a constrained minimization method. The sensitivity coefficients, i.e. gradients of the objective function and the constraints, needed for the optimization are obtained using a quasi-analytical method rather than the traditional brute force method of finite difference approximations. During the one-dimensional search of the optimization procedure, an approximate flow analysis (predicted flow) based on a first-order Taylor series expansion is used to reduce the computational cost. Finally, the sensitivity of the optimum objective function to various design parameters, which are kept constant during the optimization, is computed to predict new optimum solutions. The flow analysis of the demonstrative example are compared with the experimental data. It is shown that the method is more efficient than the traditional methods.

Proteins of unknown function in the Protein Data Bank (PDB): an inventory of true uncharacterized proteins and computational tools for their analysis.

PubMed

Nadzirin, Nurul; Firdaus-Raih, Mohd

2012-10-08

Proteins of uncharacterized functions form a large part of many of the currently available biological databases and this situation exists even in the Protein Data Bank (PDB). Our analysis of recent PDB data revealed that only 42.53% of PDB entries (1084 coordinate files) that were categorized under "unknown function" are true examples of proteins of unknown function at this point in time. The remainder 1465 entries also annotated as such appear to be able to have their annotations re-assessed, based on the availability of direct functional characterization experiments for the protein itself, or for homologous sequences or structures thus enabling computational function inference.

Multi-Physics MRI-Based Two-Layer Fluid-Structure Interaction Anisotropic Models of Human Right and Left Ventricles with Different Patch Materials: Cardiac Function Assessment and Mechanical Stress Analysis

PubMed Central

Tang, Dalin; Yang, Chun; Geva, Tal; Gaudette, Glenn; del Nido, Pedro J.

2011-01-01

Multi-physics right and left ventricle (RV/LV) fluid-structure interaction (FSI) models were introduced to perform mechanical stress analysis and evaluate the effect of patch materials on RV function. The FSI models included three different patch materials (Dacron scaffold, treated pericardium, and contracting myocardium), two-layer construction, fiber orientation, and active anisotropic material properties. The models were constructed based on cardiac magnetic resonance (CMR) images acquired from a patient with severe RV dilatation and solved by ADINA. Our results indicate that the patch model with contracting myocardium leads to decreased stress level in the patch area, improved RV function and patch area contractility. PMID:21765559

Semi-supervised clustering for parcellating brain regions based on resting state fMRI data

NASA Astrophysics Data System (ADS)

Cheng, Hewei; Fan, Yong

2014-03-01

Many unsupervised clustering techniques have been adopted for parcellating brain regions of interest into functionally homogeneous subregions based on resting state fMRI data. However, the unsupervised clustering techniques are not able to take advantage of exiting knowledge of the functional neuroanatomy readily available from studies of cytoarchitectonic parcellation or meta-analysis of the literature. In this study, we propose a semi-supervised clustering method for parcellating amygdala into functionally homogeneous subregions based on resting state fMRI data. Particularly, the semi-supervised clustering is implemented under the framework of graph partitioning, and adopts prior information and spatial consistent constraints to obtain a spatially contiguous parcellation result. The graph partitioning problem is solved using an efficient algorithm similar to the well-known weighted kernel k-means algorithm. Our method has been validated for parcellating amygdala into 3 subregions based on resting state fMRI data of 28 subjects. The experiment results have demonstrated that the proposed method is more robust than unsupervised clustering and able to parcellate amygdala into centromedial, laterobasal, and superficial parts with improved functionally homogeneity compared with the cytoarchitectonic parcellation result. The validity of the parcellation results is also supported by distinctive functional and structural connectivity patterns of the subregions and high consistency between coactivation patterns derived from a meta-analysis and functional connectivity patterns of corresponding subregions.

Explanation of asymmetric dynamics of human water consumption in arid regions: prospect theory versus expected utility theory

NASA Astrophysics Data System (ADS)

Tian, F.; Lu, Y.

2017-12-01

Based on socioeconomic and hydrological data in three arid inland basins and error analysis, the dynamics of human water consumption (HWC) are analyzed to be asymmetric, i.e., HWC increase rapidly in wet periods while maintain or decrease slightly in dry periods. Besides the qualitative analysis that in wet periods great water availability inspires HWC to grow fast but the now expanded economy is managed to sustain by over-exploitation in dry periods, two quantitative models are established and tested, based on expected utility theory (EUT) and prospect theory (PT) respectively. EUT states that humans make decisions based on the total expected utility, namely the sum of utility function multiplied by probability of each result, while PT states that the utility function is defined over gains and losses separately, and probability should be replaced by probability weighting function.

a Simulation-As Framework Facilitating Webgis Based Installation Planning

NASA Astrophysics Data System (ADS)

Zheng, Z.; Chang, Z. Y.; Fei, Y. F.

2017-09-01

Installation Planning is constrained by both natural and social conditions, especially for spatially sparse but functionally connected facilities. Simulation is important for proper deploy in space and configuration in function of facilities to make them a cohesive and supportive system to meet users' operation needs. Based on requirement analysis, we propose a framework to combine GIS and Agent simulation to overcome the shortness in temporal analysis and task simulation of traditional GIS. In this framework, Agent based simulation runs as a service on the server, exposes basic simulation functions, such as scenario configuration, simulation control, and simulation data retrieval to installation planners. At the same time, the simulation service is able to utilize various kinds of geoprocessing services in Agents' process logic to make sophisticated spatial inferences and analysis. This simulation-as-a-service framework has many potential benefits, such as easy-to-use, on-demand, shared understanding, and boosted performances. At the end, we present a preliminary implement of this concept using ArcGIS javascript api 4.0 and ArcGIS for server, showing how trip planning and driving can be carried out by agents.

Innovating Method of Existing Mechanical Product Based on TRIZ Theory

NASA Astrophysics Data System (ADS)

Zhao, Cunyou; Shi, Dongyan; Wu, Han

Main way of product development is adaptive design and variant design based on existing product. In this paper, conceptual design frame and its flow model of innovating products is put forward through combining the methods of conceptual design and TRIZ theory. Process system model of innovating design that includes requirement analysis, total function analysis and decomposing, engineering problem analysis, finding solution of engineering problem and primarily design is constructed and this establishes the base for innovating design of existing product.

Creativity and the default network: A functional connectivity analysis of the creative brain at rest☆

PubMed Central

Beaty, Roger E.; Benedek, Mathias; Wilkins, Robin W.; Jauk, Emanuel; Fink, Andreas; Silvia, Paul J.; Hodges, Donald A.; Koschutnig, Karl; Neubauer, Aljoscha C.

2014-01-01

The present research used resting-state functional magnetic resonance imaging (fMRI) to examine whether the ability to generate creative ideas corresponds to differences in the intrinsic organization of functional networks in the brain. We examined the functional connectivity between regions commonly implicated in neuroimaging studies of divergent thinking, including the inferior prefrontal cortex and the core hubs of the default network. Participants were prescreened on a battery of divergent thinking tests and assigned to high- and low-creative groups based on task performance. Seed-based functional connectivity analysis revealed greater connectivity between the left inferior frontal gyrus (IFG) and the entire default mode network in the high-creative group. The right IFG also showed greater functional connectivity with bilateral inferior parietal cortex and the left dorsolateral prefrontal cortex in the high-creative group. The results suggest that the ability to generate creative ideas is characterized by increased functional connectivity between the inferior prefrontal cortex and the default network, pointing to a greater cooperation between brain regions associated with cognitive control and low-level imaginative processes. PMID:25245940

Taxonomical and functional microbial community selection in soybean rhizosphere

PubMed Central

Mendes, Lucas W; Kuramae, Eiko E; Navarrete, Acácio A; van Veen, Johannes A; Tsai, Siu M

2014-01-01

This study addressed the selection of the rhizospheric microbial community from the bulk soil reservoir under agricultural management of soybean in Amazon forest soils. We used a shotgun metagenomics approach to investigate the taxonomic and functional diversities of microbial communities in the bulk soil and in the rhizosphere of soybean plants and tested the validity of neutral and niche theories to explain the rhizosphere community assembly processes. Our results showed a clear selection at both taxonomic and functional levels operating in the assembly of the soybean rhizosphere community. The taxonomic analysis revealed that the rhizosphere community is a subset of the bulk soil community. Species abundance in rhizosphere fits the log-normal distribution model, which is an indicator of the occurrence of niche-based processes. In addition, the data indicate that the rhizosphere community is selected based on functional cores related to the metabolisms of nitrogen, iron, phosphorus and potassium, which are related to benefits to the plant, such as growth promotion and nutrition. The network analysis including bacterial groups and functions was less complex in rhizosphere, suggesting the specialization of some specific metabolic pathways. We conclude that the assembly of the microbial community in the rhizosphere is based on niche-based processes as a result of the selection power of the plant and other environmental factors. PMID:24553468

NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology.

PubMed

Wei, Qing; Khan, Ishita K; Ding, Ziyun; Yerneni, Satwica; Kihara, Daisuke

2017-03-20

The number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation. With the well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for representing gene functions. To understand relationship and similarity between GO annotations of genes, it is important to have a convenient pipeline that quantifies and visualizes the GO function analyses in a systematic fashion. NaviGO is a web-based tool for interactive visualization, retrieval, and computation of functional similarity and associations of GO terms and genes. Similarity of GO terms and gene functions is quantified with six different scores including protein-protein interaction and context based association scores we have developed in our previous works. Interactive navigation of the GO function space provides intuitive and effective real-time visualization of functional groupings of GO terms and genes as well as statistical analysis of enriched functions. We developed NaviGO, which visualizes and analyses functional similarity and associations of GO terms and genes. The NaviGO webserver is freely available at: http://kiharalab.org/web/navigo .

The analysis of mathematics teachers' learning on algebra function limit material based on teaching experience difference

NASA Astrophysics Data System (ADS)

Ma'rufi, Budayasa, I. Ketut; Juniati, Dwi

2017-08-01

The aim of this study was to describe the analysis of mathematics teachers' learning on algebra function limit material based on teaching experience difference. The purpose of this study is to describe the analysis of mathematics teacher's learning on limit algebraic functions in terms of the differences of teaching experience. Learning analysis focused on Pedagogical Content Knowledge (PCK) of teachers in mathematics on limit algebraic functions related to the knowledge of pedagogy. PCK of teachers on limit algebraic function is a type of specialized knowledge for teachers on how to teach limit algebraic function that can be understood by students. Subjects are two high school mathematics teacher who has difference of teaching experience they are one Novice Teacher (NP) and one Experienced Teacher (ET). Data are collected through observation of learning in the class, videos of learning, and then analyzed using qualitative analysis. Teacher's knowledge of Pedagogic defined as a knowledge and understanding of teacher about planning and organizing of learning, and application of learning strategy. The research results showed that the Knowledge of Pedagogy on subject NT in mathematics learning on the material of limit function algebra showed that the subject NT tended to describe procedurally, without explaining the reasons why such steps were used, asking questions which tended to be monotonous not be guiding and digging deeper, and less varied in the use of learning strategies while subject ET gave limited guidance and opportunities to the students to find their own answers, exploit the potential of students to answer questions, provide an opportunity for students to interact and work in groups, and subject ET tended to combine conceptual and procedural explanation.

NbIT - A New Information Theory-Based Analysis of Allosteric Mechanisms Reveals Residues that Underlie Function in the Leucine Transporter LeuT

PubMed Central

LeVine, Michael V.; Weinstein, Harel

2014-01-01

Complex networks of interacting residues and microdomains in the structures of biomolecular systems underlie the reliable propagation of information from an input signal, such as the concentration of a ligand, to sites that generate the appropriate output signal, such as enzymatic activity. This information transduction often carries the signal across relatively large distances at the molecular scale in a form of allostery that is essential for the physiological functions performed by biomolecules. While allosteric behaviors have been documented from experiments and computation, the mechanism of this form of allostery proved difficult to identify at the molecular level. Here, we introduce a novel analysis framework, called N-body Information Theory (NbIT) analysis, which is based on information theory and uses measures of configurational entropy in a biomolecular system to identify microdomains and individual residues that act as (i)-channels for long-distance information sharing between functional sites, and (ii)-coordinators that organize dynamics within functional sites. Application of the new method to molecular dynamics (MD) trajectories of the occluded state of the bacterial leucine transporter LeuT identifies a channel of allosteric coupling between the functionally important intracellular gate and the substrate binding sites known to modulate it. NbIT analysis is shown also to differentiate residues involved primarily in stabilizing the functional sites, from those that contribute to allosteric couplings between sites. NbIT analysis of MD data thus reveals rigorous mechanistic elements of allostery underlying the dynamics of biomolecular systems. PMID:24785005

Gene Networks and Functional Features of Gravitropic response in Rice Shoot Bases

NASA Astrophysics Data System (ADS)

Hu, Liwei; Zang, Aiping; Ai, Qianru; Chen, Haiying; Li, Lin; Li, Rui; Su, Feng; Chen, Xijiang; Rong, Hui; Dou, Xianying; Reinhold-Hurek, Barbara; Li, Qi; Cai, Weiming

To delineate key genes and the corresponding physiological functions as well as the coordina-tion of genes involved in the gravitropism of rice shoot bases, we used whole-genome microarray analysis of upper and lower parts of rice shoot bases at 0.5 h and 6 h after gravistimulation. And bio-information analysis was applied including GO-analysis, expression tendency and net-work analysis. In the lower shoot bases, auxin-mediated signaling pathway and glutathione transferase activity with the biggest enrichment were activated at 0.5 h, while cytokinin stimu-lus and photosynthesis were activated at 6 h. Meanwhile, several processes were suppressed in the lower shoot bases, including: xyloglucan:xyloglucosyl transferase activity, glucan metabolic processes, and ATPase activity at 0.5 h; and tRNA isopentenyltransferase activity, and chiti-nase activity, etc. at 6 h. Gene expression profile responding to gravistimulation suggested that the asymmetrically activation of several phytohormone signaling pathways including auxin, gib-berellin and cytokinin brassinolide ethylene and cytokinin-related genes were involved in the differentially growth between the upper and lower parts of rice shoot bases, and so do cell wall-related genes. Topological analysis of the coexpression networks revealed the core statue of AY177699.1(apetala3-like protein) and AK105103.1 at 0.5 h; AK062612.1 (ethylene response factor) and AK099932.1 (lectin-like receptor kinase 72) at 6 h. All the core factors have the function "response to endogenous stimulus". Additionally, AK108057.1(similar to germin-like protein precursor) was discovered as the most important core gene in the upper shoot bases in 6h after gravistimualtion while AK067424.1(cellulose synthase-like protein), AK120101.1 (Zinc finger, B-box domain containing protein) and CR278698 (ATPase associated with various cel-lular activities cellulose synthase-like protein) contribute equally to gravitropic response in the lower shoot bases.

Characterization of Microbiota in Children with Chronic Functional Constipation

PubMed Central

de Meij, Tim G. J.; de Groot, Evelien F. J.; Eck, Anat; Budding, Andries E.; Kneepkens, C. M. Frank; Benninga, Marc A.; van Bodegraven, Adriaan A.; Savelkoul, Paul H. M.

2016-01-01

Objectives Disruption of the intestinal microbiota is considered an etiological factor in pediatric functional constipation. Scientifically based selection of potential beneficial probiotic strains in functional constipation therapy is not feasible due to insufficient knowledge of microbiota composition in affected subjects. The aim of this study was to describe microbial composition and diversity in children with functional constipation, compared to healthy controls. Study Design Fecal samples from 76 children diagnosed with functional constipation according to the Rome III criteria (median age 8.0 years; range 4.2–17.8) were analyzed by IS-pro, a PCR-based microbiota profiling method. Outcome was compared with intestinal microbiota profiles of 61 healthy children (median 8.6 years; range 4.1–17.9). Microbiota dissimilarity was depicted by principal coordinate analysis (PCoA), diversity was calculated by Shannon diversity index. To determine the most discriminative species, cross validated logistic ridge regression was performed. Results Applying total microbiota profiles (all phyla together) or per phylum analysis, no disease-specific separation was observed by PCoA and by calculation of diversity indices. By ridge regression, however, functional constipation and controls could be discriminated with 82% accuracy. Most discriminative species were Bacteroides fragilis, Bacteroides ovatus, Bifidobacterium longum, Parabacteroides species (increased in functional constipation) and Alistipes finegoldii (decreased in functional constipation). Conclusions None of the commonly used unsupervised statistical methods allowed for microbiota-based discrimination of children with functional constipation and controls. By ridge regression, however, both groups could be discriminated with 82% accuracy. Optimization of microbiota-based interventions in constipated children warrants further characterization of microbial signatures linked to clinical subgroups of functional constipation. PMID:27760208

Using Discrete Loss Functions and Weighted Kappa for Classification: An Illustration Based on Bayesian Network Analysis

ERIC Educational Resources Information Center

Zwick, Rebecca; Lenaburg, Lubella

2009-01-01

In certain data analyses (e.g., multiple discriminant analysis and multinomial log-linear modeling), classification decisions are made based on the estimated posterior probabilities that individuals belong to each of several distinct categories. In the Bayesian network literature, this type of classification is often accomplished by assigning…

Hepatobiliary MRI: Signal intensity based assessment of liver function correlated to 13C-Methacetin breath test.

PubMed

Haimerl, Michael; Probst, Ute; Poelsterl, Stefanie; Beyer, Lukas; Fellner, Claudia; Selgrad, Michael; Hornung, Matthias; Stroszczynski, Christian; Wiggermann, Philipp

2018-06-13

Gadoxetic acid (Gd-EOB-DTPA) is a paramagnetic MRI contrast agent with raising popularity and has been used for evaluation of imaging-based liver function in recent years. In order to verify whether liver function as determined by real-time breath analysis using the intravenous administration of 13 C-methacetin can be estimated quantitatively from Gd-EOB-DTPA-enhanced MRI using signal intensity (SI) values. 110 patients underwent Gd-EOB-DTPA-enhanced 3-T MRI and, for the evaluation of liver function, a 13 C-methacetin breath test ( 13 C-MBT). SI values from before (SI pre ) and 20 min after (SI post ) contrast media injection were acquired by T1-weighted volume-interpolated breath-hold examination (VIBE) sequences with fat suppression. The relative enhancement (RE) between the plain and contrast-enhanced SI values was calculated and evaluated in a correlation analysis of 13 C-MBT values to SI post and RE to obtain a SI-based estimation of 13 C-MBT values. The simple regression model showed a log-linear correlation of 13 C-MBT values with SI post and RE (p < 0.001). Stratified by 3 different categories of 13 C-MBT readouts, there was a constant significant decrease in both SI post (p ≤ 0.002) and RE (p ≤ 0.033) with increasing liver disease progression as assessed by the 13 C-MBT. Liver function as determined using real-time 13 C-methacetin breath analysis can be estimated quantitatively from Gd-EOB-DTPA-enhanced MRI using SI-based indices.

Perspectives on Functional (Behavioral) Assessment

ERIC Educational Resources Information Center

Dunlap, Glen; Kern, Lee

2018-01-01

In the 25 years since the publication of the article reprinted in this issue of "Behavioral Disorders", a tremendous amount of research and opinion has been published on the topics of functional analysis, functional assessment, and assessment-based interventions. In this commentary, we reflect on the context in which our original work…

Functional Analysis and Treatment of Multiply Controlled Inappropriate Mealtime Behavior

ERIC Educational Resources Information Center

Bachmeyer, Melanie H.; Piazza, Cathleen C.; Fredrick, Laura D.; Reed, Gregory K.; Rivas, Kristi D.; Kadey, Heather J.

2009-01-01

Functional analyses identified children whose inappropriate mealtime behavior was maintained by escape and adult attention. Function-based extinction procedures were tested individually and in combination. Attention extinction alone did not result in decreases in inappropriate mealtime behavior or a significant increase in acceptance. By contrast,…

Functional Analysis and Treatment of Problem Behavior in Early Education Classrooms

ERIC Educational Resources Information Center

Greer, Brian D.; Neidert, Pamela L.; Dozier, Claudia L.; Payne, Steven W.; Zonneveld, Kimberley L. M.; Harper, Amy M.

2013-01-01

We conducted functional analyses (FA) with 4 typically developing preschool children during ongoing classroom activities and evaluated treatments that were based on FA results. Results of each child's FA suggested social-positive reinforcement functions, and differential reinforcement of alternative behavior plus time-out was effective in…

Functional Data Analysis in NTCP Modeling: A New Method to Explore the Radiation Dose-Volume Effects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Benadjaoud, Mohamed Amine, E-mail: mohamedamine.benadjaoud@gustaveroussy.fr; Université Paris sud, Le Kremlin-Bicêtre; Institut Gustave Roussy, Villejuif

2014-11-01

Purpose/Objective(s): To describe a novel method to explore radiation dose-volume effects. Functional data analysis is used to investigate the information contained in differential dose-volume histograms. The method is applied to the normal tissue complication probability modeling of rectal bleeding (RB) for patients irradiated in the prostatic bed by 3-dimensional conformal radiation therapy. Methods and Materials: Kernel density estimation was used to estimate the individual probability density functions from each of the 141 rectum differential dose-volume histograms. Functional principal component analysis was performed on the estimated probability density functions to explore the variation modes in the dose distribution. The functional principalmore » components were then tested for association with RB using logistic regression adapted to functional covariates (FLR). For comparison, 3 other normal tissue complication probability models were considered: the Lyman-Kutcher-Burman model, logistic model based on standard dosimetric parameters (LM), and logistic model based on multivariate principal component analysis (PCA). Results: The incidence rate of grade ≥2 RB was 14%. V{sub 65Gy} was the most predictive factor for the LM (P=.058). The best fit for the Lyman-Kutcher-Burman model was obtained with n=0.12, m = 0.17, and TD50 = 72.6 Gy. In PCA and FLR, the components that describe the interdependence between the relative volumes exposed at intermediate and high doses were the most correlated to the complication. The FLR parameter function leads to a better understanding of the volume effect by including the treatment specificity in the delivered mechanistic information. For RB grade ≥2, patients with advanced age are significantly at risk (odds ratio, 1.123; 95% confidence interval, 1.03-1.22), and the fits of the LM, PCA, and functional principal component analysis models are significantly improved by including this clinical factor. Conclusion: Functional data analysis provides an attractive method for flexibly estimating the dose-volume effect for normal tissues in external radiation therapy.« less

Gender similarities and differences in brain activation strategies: Voxel-based meta-analysis on fMRI studies.

PubMed

AlRyalat, Saif Aldeen

2017-01-01

Gender similarities and differences have long been a matter of debate in almost all human research, especially upon reaching the discussion about brain functions. This large scale meta-analysis was performed on functional MRI studies. It included more than 700 active brain foci from more than 70 different experiments to study gender related similarities and differences in brain activation strategies for three of the main brain functions: Visual-spatial cognition, memory, and emotion. Areas that are significantly activated by both genders (i.e. core areas) for the tested brain function are mentioned, whereas those areas significantly activated exclusively in one gender are the gender specific areas. During visual-spatial cognition task, and in addition to the core areas, males significantly activated their left superior frontal gyrus, compared with left superior parietal lobule in females. For memory tasks, several different brain areas activated by each gender, but females significantly activated two areas from the limbic system during memory retrieval tasks. For emotional task, males tend to recruit their bilateral prefrontal regions, whereas females tend to recruit their bilateral amygdalae. This meta-analysis provides an overview based on functional MRI studies on how males and females use their brain.

Exploring the relationships among performance-based functional ability, self-rated disability, perceived instrumental support, and depression: a structural equation model analysis.

PubMed

Weil, Joyce; Hutchinson, Susan R; Traxler, Karen

2014-11-01

Data from the Women's Health and Aging Study were used to test a model of factors explaining depressive symptomology. The primary purpose of the study was to explore the association between performance-based measures of functional ability and depression and to examine the role of self-rated physical difficulties and perceived instrumental support in mediating the relationship between performance-based functioning and depression. The inclusion of performance-based measures allows for the testing of functional ability as a clinical precursor to disability and depression: a critical, but rarely examined, association in the disablement process. Structural equation modeling supported the overall fit of the model and found an indirect relationship between performance-based functioning and depression, with perceived physical difficulties serving as a significant mediator. Our results highlight the complementary nature of performance-based and self-rated measures and the importance of including perception of self-rated physical difficulties when examining depression in older persons. © The Author(s) 2014.

False dyssynchrony: problem with image-based cardiac functional analysis using x-ray computed tomography

NASA Astrophysics Data System (ADS)

Kidoh, Masafumi; Shen, Zeyang; Suzuki, Yuki; Ciuffo, Luisa; Ashikaga, Hiroshi; Fung, George S. K.; Otake, Yoshito; Zimmerman, Stefan L.; Lima, Joao A. C.; Higuchi, Takahiro; Lee, Okkyun; Sato, Yoshinobu; Becker, Lewis C.; Fishman, Elliot K.; Taguchi, Katsuyuki

2017-03-01

We have developed a digitally synthesized patient which we call "Zach" (Zero millisecond Adjustable Clinical Heart) phantom, which allows for an access to the ground truth and assessment of image-based cardiac functional analysis (CFA) using CT images with clinically realistic settings. The study using Zach phantom revealed a major problem with image-based CFA: "False dyssynchrony." Even though the true motion of wall segments is in synchrony, it may appear to be dyssynchrony with the reconstructed cardiac CT images. It is attributed to how cardiac images are reconstructed and how wall locations are updated over cardiac phases. The presence and the degree of false dyssynchrony may vary from scan-to-scan, which could degrade the accuracy and the repeatability (or precision) of image-based CT-CFA exams.

Gene Set−Based Integrative Analysis Revealing Two Distinct Functional Regulation Patterns in Four Common Subtypes of Epithelial Ovarian Cancer

PubMed Central

Chang, Chia-Ming; Chuang, Chi-Mu; Wang, Mong-Lien; Yang, Yi-Ping; Chuang, Jen-Hua; Yang, Ming-Jie; Yen, Ming-Shyen; Chiou, Shih-Hwa; Chang, Cheng-Chang

2016-01-01

Clear cell (CCC), endometrioid (EC), mucinous (MC) and high-grade serous carcinoma (SC) are the four most common subtypes of epithelial ovarian carcinoma (EOC). The widely accepted dualistic model of ovarian carcinogenesis divided EOCs into type I and II categories based on the molecular features. However, this hypothesis has not been experimentally demonstrated. We carried out a gene set-based analysis by integrating the microarray gene expression profiles downloaded from the publicly available databases. These quantified biological functions of EOCs were defined by 1454 Gene Ontology (GO) term and 674 Reactome pathway gene sets. The pathogenesis of the four EOC subtypes was investigated by hierarchical clustering and exploratory factor analysis. The patterns of functional regulation among the four subtypes containing 1316 cases could be accurately classified by machine learning. The results revealed that the ERBB and PI3K-related pathways played important roles in the carcinogenesis of CCC, EC and MC; while deregulation of cell cycle was more predominant in SC. The study revealed that two different functional regulation patterns exist among the four EOC subtypes, which were compatible with the type I and II classifications proposed by the dualistic model of ovarian carcinogenesis. PMID:27527159

Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers

PubMed Central

Hsiao, Tzu-Hung; Chiu, Yu-Chiao; Hsu, Pei-Yin; Lu, Tzu-Pin; Lai, Liang-Chuan; Tsai, Mong-Hsun; Huang, Tim H.-M.; Chuang, Eric Y.; Chen, Yidong

2016-01-01

Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due to intensive computation, however, these methods rely heavily on prior knowledge and are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis to systematically identify genome-wide modulation of interaction networks. Based on a novel statistical test employing conjugate Fisher transformations of correlation coefficients, MAGIC features fast computation and adaption to variations of clinical cohorts. In simulated datasets MAGIC achieved greatly improved computation efficiency and overall superior performance than the MI-based method. We applied MAGIC to construct the estrogen receptor (ER) modulated gene and gene set (representing biological function) interaction networks in breast cancer. Several novel interaction hubs and functional interactions were discovered. ER+ dependent interaction between TGFβ and NFκB was further shown to be associated with patient survival. The findings were verified in independent datasets. Using MAGIC, we also assessed the essential roles of ER modulation in another hormonal cancer, ovarian cancer. Overall, MAGIC is a systematic framework for comprehensively identifying and constructing the modulated interaction networks in a whole-genome landscape. MATLAB implementation of MAGIC is available for academic uses at https://github.com/chiuyc/MAGIC. PMID:26972162

Meshfree truncated hierarchical refinement for isogeometric analysis

NASA Astrophysics Data System (ADS)

Atri, H. R.; Shojaee, S.

2018-05-01

In this paper truncated hierarchical B-spline (THB-spline) is coupled with reproducing kernel particle method (RKPM) to blend advantages of the isogeometric analysis and meshfree methods. Since under certain conditions, the isogeometric B-spline and NURBS basis functions are exactly represented by reproducing kernel meshfree shape functions, recursive process of producing isogeometric bases can be omitted. More importantly, a seamless link between meshfree methods and isogeometric analysis can be easily defined which provide an authentic meshfree approach to refine the model locally in isogeometric analysis. This procedure can be accomplished using truncated hierarchical B-splines to construct new bases and adaptively refine them. It is also shown that the THB-RKPM method can provide efficient approximation schemes for numerical simulations and represent a promising performance in adaptive refinement of partial differential equations via isogeometric analysis. The proposed approach for adaptive locally refinement is presented in detail and its effectiveness is investigated through well-known benchmark examples.

Interactive Design Strategy for a Multi-Functional PAMAM Dendrimer-Based Nano-Therapeutic Using Computational Models and Experimental Analysis

PubMed Central

Lee, Inhan; Williams, Christopher R.; Athey, Brian D.; Baker, James R.

2010-01-01

Molecular dynamics simulations of nano-therapeutics as a final product and of all intermediates in the process of generating a multi-functional nano-therapeutic based on a poly(amidoamine) (PAMAM) dendrimer were performed along with chemical analyses of each of them. The actual structures of the dendrimers were predicted, based on potentiometric titration, gel permeation chromatography, and NMR. The chemical analyses determined the numbers of functional molecules, based on the actual structure of the dendrimer. Molecular dynamics simulations calculated the configurations of the intermediates and the radial distributions of functional molecules, based on their numbers. This interactive process between the simulation results and the chemical analyses provided a further strategy to design the next reaction steps and to gain insight into the products at each chemical reaction step. PMID:20700476

Optimizing donor heart outcome after prolonged storage with endothelial function analysis and continuous perfusion.

PubMed

Poston, Robert S; Gu, Junyan; Prastein, Deyanira; Gage, Fred; Hoffman, John W; Kwon, Michael; Azimzadeh, Agnes; Pierson, Richard N; Griffith, Bartley P

2004-10-01

By minimizing tissue ischemia, continuous perfusion (CP) during organ transport may increase the safety of "marginal donors." My colleagues and I investigated whether an analysis of donor heart viability predicts recovery of grafts challenged with a 24-hour preservation interval. Dog hearts underwent cold static storage (CS) for 8 hours (n = 8) or 24 hours (n = 2) or CP for 24 hours with cold asanguinous, oxygenated solution (n = 8). Myocardial systolic and diastolic function and oxygen and lactate consumption were assessed at base line, during CP, and after Langendorff blood reperfusion. Base line endothelial function was evaluated by the percentage transcoronary change ([coronary sinus - aorta]/aorta) in myeloperoxidase and by platelet function and coronary flow reserve after 20 seconds of coronary artery occlusion. During CP, the endothelium was assessed by transcoronary protein release and coronary resistance. Edema was assessed by weight gain and histology. Base line systolic and metabolic functions showed no relation to post-Langendorff function. Compared with CS, CP resulted in a greater recovery in systolic function (87% +/- 35% vs 65% +/- 15% of baseline; p = 0.05) and a shorter interval required for lactate consumption to exceed production (7.0 +/- 6.8 minutes vs 15.0 +/- 8.9 minutes; p = 0.06). Endothelial function was heterogeneous: coronary flow reserve, 2.7 +/- 0.7; percentage change in myeloperoxidase, -8.4% +/- 6.8%; and change in platelet function, 4.3% +/- 3.5%, as determined by thromboelastography angle at base line. Protein release during CP for 24 hours was 8.3 +/- 7.1 g. Two factors predicted more than 75% systolic pressure generation recovery: use of CP and normal endothelial function (p = 0.05; Fisher's exact test). However, CP led to edema according to histology, weight gain (72 +/- 29 g), and impaired diastolic function versus CS (end-diastolic pressure-volume relationship, 1.4 +/- 0.4 mm Hg/mL vs 0.8 +/- 0.3 mm Hg/mL; p = 0.08). Better systolic function despite 16 hours' more preservation than cold storage corroborates the idea that CP supports aerobic metabolism at physiologically important levels. Viability analysis focused on endothelial function and identified organs that were able to tolerate this 24-hour preservation interval.

A SVM-based quantitative fMRI method for resting-state functional network detection.

PubMed

Song, Xiaomu; Chen, Nan-kuei

2014-09-01

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies. Copyright © 2014 Elsevier Inc. All rights reserved.

Space station data system analysis/architecture study. Task 1: Functional requirements definition, DR-5. Appendix: Requirements data base

NASA Technical Reports Server (NTRS)

1985-01-01

Appendix A contains data that characterize the system functions in sufficient depth as to determine the requirements for the Space Station Data System (SSDS). This data is in the form of: (1) top down traceability report; (2) bottom up traceability report; (3) requirements data sheets; and (4) cross index of requirements paragraphs of the source documents and the requirements numbers. A data base users guide is included that interested parties can use to access the requirements data base and get up to date information about the functions.

A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis

PubMed Central

Down, Thomas A.; Rakyan, Vardhman K.; Turner, Daniel J.; Flicek, Paul; Li, Heng; Kulesha, Eugene; Gräf, Stefan; Johnson, Nathan; Herrero, Javier; Tomazou, Eleni M.; Thorne, Natalie P.; Bäckdahl, Liselotte; Herberth, Marlis; Howe, Kevin L.; Jackson, David K.; Miretti, Marcos M.; Marioni, John C.; Birney, Ewan; Hubbard, Tim J. P.; Durbin, Richard; Tavaré, Simon; Beck, Stephan

2009-01-01

DNA methylation is an indispensible epigenetic modification of mammalian genomes. Consequently there is great interest in strategies for genome-wide/whole-genome DNA methylation analysis, and immunoprecipitation-based methods have proven to be a powerful option. Such methods are rapidly shifting the bottleneck from data generation to data analysis, necessitating the development of better analytical tools. Until now, a major analytical difficulty associated with immunoprecipitation-based DNA methylation profiling has been the inability to estimate absolute methylation levels. Here we report the development of a novel cross-platform algorithm – Bayesian Tool for Methylation Analysis (Batman) – for analyzing Methylated DNA Immunoprecipitation (MeDIP) profiles generated using arrays (MeDIP-chip) or next-generation sequencing (MeDIP-seq). The latter is an approach we have developed to elucidate the first high-resolution whole-genome DNA methylation profile (DNA methylome) of any mammalian genome. MeDIP-seq/MeDIP-chip combined with Batman represent robust, quantitative, and cost-effective functional genomic strategies for elucidating the function of DNA methylation. PMID:18612301

Scenario-Based Case Study Method and the Functionality of the Section Called "From Production to Consumption" from the Perspective of Primary School Students

ERIC Educational Resources Information Center

Taneri, Ahu

2018-01-01

In this research, the aim was showing the evaluation of students on scenario-based case study method and showing the functionality of the studied section called "from production to consumption". Qualitative research method and content analysis were used to reveal participants' experiences and reveal meaningful relations regarding…

Using Stimulus Equivalence-Based Instruction to Teach Graduate Students in Applied Behavior Analysis to Interpret Operant Functions of Behavior

ERIC Educational Resources Information Center

Albright, Leif; Schnell, Lauren; Reeve, Kenneth F.; Sidener, Tina M.

2016-01-01

Stimulus equivalence-based instruction (EBI) was used to teach four, 4-member classes representing functions of behavior to ten graduate students. The classes represented behavior maintained by attention (Class 1), escape (Class 2), access to tangibles (Class 3), and automatic reinforcement (Class 4). Stimuli within each class consisted of a…

REVIEWS OF TOPICAL PROBLEMS: Nonlinear dynamics of the brain: emotion and cognition

NASA Astrophysics Data System (ADS)

Rabinovich, Mikhail I.; Muezzinoglu, M. K.

2010-07-01

Experimental investigations of neural system functioning and brain activity are standardly based on the assumption that perceptions, emotions, and cognitive functions can be understood by analyzing steady-state neural processes and static tomographic snapshots. The new approaches discussed in this review are based on the analysis of transient processes and metastable states. Transient dynamics is characterized by two basic properties, structural stability and information sensitivity. The ideas and methods that we discuss provide an explanation for the occurrence of and successive transitions between metastable states observed in experiments, and offer new approaches to behavior analysis. Models of the emotional and cognitive functions of the brain are suggested. The mathematical object that represents the observed transient brain processes in the phase space of the model is a structurally stable heteroclinic channel. The possibility of using the suggested models to construct a quantitative theory of some emotional and cognitive functions is illustrated.

Analysis of mammalian gene function through broad based phenotypic screens across a consortium of mouse clinics

PubMed Central

Adams, David J; Adams, Niels C; Adler, Thure; Aguilar-Pimentel, Antonio; Ali-Hadji, Dalila; Amann, Gregory; André, Philippe; Atkins, Sarah; Auburtin, Aurelie; Ayadi, Abdel; Becker, Julien; Becker, Lore; Bedu, Elodie; Bekeredjian, Raffi; Birling, Marie-Christine; Blake, Andrew; Bottomley, Joanna; Bowl, Mike; Brault, Véronique; Busch, Dirk H; Bussell, James N; Calzada-Wack, Julia; Cater, Heather; Champy, Marie-France; Charles, Philippe; Chevalier, Claire; Chiani, Francesco; Codner, Gemma F; Combe, Roy; Cox, Roger; Dalloneau, Emilie; Dierich, André; Di Fenza, Armida; Doe, Brendan; Duchon, Arnaud; Eickelberg, Oliver; Esapa, Chris T; El Fertak, Lahcen; Feigel, Tanja; Emelyanova, Irina; Estabel, Jeanne; Favor, Jack; Flenniken, Ann; Gambadoro, Alessia; Garrett, Lilian; Gates, Hilary; Gerdin, Anna-Karin; Gkoutos, George; Greenaway, Simon; Glasl, Lisa; Goetz, Patrice; Da Cruz, Isabelle Goncalves; Götz, Alexander; Graw, Jochen; Guimond, Alain; Hans, Wolfgang; Hicks, Geoff; Hölter, Sabine M; Höfler, Heinz; Hancock, John M; Hoehndorf, Robert; Hough, Tertius; Houghton, Richard; Hurt, Anja; Ivandic, Boris; Jacobs, Hughes; Jacquot, Sylvie; Jones, Nora; Karp, Natasha A; Katus, Hugo A; Kitchen, Sharon; Klein-Rodewald, Tanja; Klingenspor, Martin; Klopstock, Thomas; Lalanne, Valerie; Leblanc, Sophie; Lengger, Christoph; le Marchand, Elise; Ludwig, Tonia; Lux, Aline; McKerlie, Colin; Maier, Holger; Mandel, Jean-Louis; Marschall, Susan; Mark, Manuel; Melvin, David G; Meziane, Hamid; Micklich, Kateryna; Mittelhauser, Christophe; Monassier, Laurent; Moulaert, David; Muller, Stéphanie; Naton, Beatrix; Neff, Frauke; Nolan, Patrick M; Nutter, Lauryl MJ; Ollert, Markus; Pavlovic, Guillaume; Pellegata, Natalia S; Peter, Emilie; Petit-Demoulière, Benoit; Pickard, Amanda; Podrini, Christine; Potter, Paul; Pouilly, Laurent; Puk, Oliver; Richardson, David; Rousseau, Stephane; Quintanilla-Fend, Leticia; Quwailid, Mohamed M; Racz, Ildiko; Rathkolb, Birgit; Riet, Fabrice; Rossant, Janet; Roux, Michel; Rozman, Jan; Ryder, Ed; Salisbury, Jennifer; Santos, Luis; Schäble, Karl-Heinz; Schiller, Evelyn; Schrewe, Anja; Schulz, Holger; Steinkamp, Ralf; Simon, Michelle; Stewart, Michelle; Stöger, Claudia; Stöger, Tobias; Sun, Minxuan; Sunter, David; Teboul, Lydia; Tilly, Isabelle; Tocchini-Valentini, Glauco P; Tost, Monica; Treise, Irina; Vasseur, Laurent; Velot, Emilie; Vogt-Weisenhorn, Daniela; Wagner, Christelle; Walling, Alison; Weber, Bruno; Wendling, Olivia; Westerberg, Henrik; Willershäuser, Monja; Wolf, Eckhard; Wolter, Anne; Wood, Joe; Wurst, Wolfgang; Yildirim, Ali Önder; Zeh, Ramona; Zimmer, Andreas; Zimprich, Annemarie

2015-01-01

The function of the majority of genes in the mouse and human genomes remains unknown. The mouse ES cell knockout resource provides a basis for characterisation of relationships between gene and phenotype. The EUMODIC consortium developed and validated robust methodologies for broad-based phenotyping of knockouts through a pipeline comprising 20 disease-orientated platforms. We developed novel statistical methods for pipeline design and data analysis aimed at detecting reproducible phenotypes with high power. We acquired phenotype data from 449 mutant alleles, representing 320 unique genes, of which half had no prior functional annotation. We captured data from over 27,000 mice finding that 83% of the mutant lines are phenodeviant, with 65% demonstrating pleiotropy. Surprisingly, we found significant differences in phenotype annotation according to zygosity. Novel phenotypes were uncovered for many genes with unknown function providing a powerful basis for hypothesis generation and further investigation in diverse systems. PMID:26214591

Grid-based Continual Analysis of Molecular Interior for Drug Discovery, QSAR and QSPR.

PubMed

Potemkin, Andrey V; Grishina, Maria A; Potemkin, Vladimir A

2017-01-01

In 1979, R.D.Cramer and M.Milne made a first realization of 3D comparison of molecules by aligning them in space and by mapping their molecular fields to a 3D grid. Further, this approach was developed as the DYLOMMS (Dynamic Lattice- Oriented Molecular Modelling System) approach. In 1984, H.Wold and S.Wold proposed the use of partial least squares (PLS) analysis, instead of principal component analysis, to correlate the field values with biological activities. Then, in 1988, the method which was called CoMFA (Comparative Molecular Field Analysis) was introduced and the appropriate software became commercially available. Since 1988, a lot of 3D QSAR methods, algorithms and their modifications are introduced for solving of virtual drug discovery problems (e.g., CoMSIA, CoMMA, HINT, HASL, GOLPE, GRID, PARM, Raptor, BiS, CiS, ConGO,). All the methods can be divided into two groups (classes):1. Methods studying the exterior of molecules; 2) Methods studying the interior of molecules. A series of grid-based computational technologies for Continual Molecular Interior analysis (CoMIn) are invented in the current paper. The grid-based analysis is fulfilled by means of a lattice construction analogously to many other grid-based methods. The further continual elucidation of molecular structure is performed in various ways. (i) In terms of intermolecular interactions potentials. This can be represented as a superposition of Coulomb, Van der Waals interactions and hydrogen bonds. All the potentials are well known continual functions and their values can be determined in all lattice points for a molecule. (ii) In the terms of quantum functions such as electron density distribution, Laplacian and Hamiltonian of electron density distribution, potential energy distribution, the highest occupied and the lowest unoccupied molecular orbitals distribution and their superposition. To reduce time of calculations using quantum methods based on the first principles, an original quantum free-orbital approach AlteQ is proposed. All the functions can be calculated using a quantum approach at a sufficient level of theory and their values can be determined in all lattice points for a molecule. Then, the molecules of a dataset can be superimposed in the lattice for the maximal coincidence (or minimal deviations) of the potentials (i) or the quantum functions (ii). The methods and criteria of the superimposition are discussed. After that a functional relationship between biological activity or property and characteristics of potentials (i) or functions (ii) is created. The methods of the quantitative relationship construction are discussed. New approaches for rational virtual drug design based on the intermolecular potentials and quantum functions are invented. All the invented methods are realized at www.chemosophia.com web page. Therefore, a set of 3D QSAR approaches for continual molecular interior study giving a lot of opportunities for virtual drug discovery, virtual screening and ligand-based drug design are invented. The continual elucidation of molecular structure is performed in the terms of intermolecular interactions potentials and in the terms of quantum functions such as electron density distribution, Laplacian and Hamiltonian of electron density distribution, potential energy distribution, the highest occupied and the lowest unoccupied molecular orbitals distribution and their superposition. To reduce time of calculations using quantum methods based on the first principles, an original quantum free-orbital approach AlteQ is proposed. The methods of the quantitative relationship construction are discussed. New approaches for rational virtual drug design based on the intermolecular potentials and quantum functions are invented. All the invented methods are realized at www.chemosophia.com web page. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

On one-dimensional stretching functions for finite-difference calculations. [computational fluid dynamics

NASA Technical Reports Server (NTRS)

Vinokur, M.

1979-01-01

The class of one-dimensional stretching functions used in finite-difference calculations is studied. For solutions containing a highly localized region of rapid variation, simple criteria for a stretching function are derived using a truncation error analysis. These criteria are used to investigate two types of stretching functions. One is an interior stretching function, for which the location and slope of an interior clustering region are specified. The simplest such function satisfying the criteria is found to be one based on the inverse hyperbolic sine. The other type of function is a two-sided stretching function, for which the arbitrary slopes at the two ends of the one-dimensional interval are specified. The simplest such general function is found to be one based on the inverse tangent.

[Tourism function zoning of Jinyintan Grassland Scenic Area in Qinghai Province based on ecological sensitivity analysis].

PubMed

Zhong, Lin-sheng; Tang, Cheng-cai; Guo, Hua

2010-07-01

Based on the statistical data of natural ecology and social economy in Jinyintan Grassland Scenic Area in Qinghai Province in 2008, an evaluation index system for the ecological sensitivity of this area was established from the aspects of protected area rank, vegetation type, slope, and land use type. The ecological sensitivity of the sub-areas with higher tourism value and ecological function in the area was evaluated, and the tourism function zoning of these sub-areas was made by the technology of GIS and according to the analysis of eco-environmental characteristics and ecological sensitivity of each sensitive sub-area. It was suggested that the Jinyintan Grassland Scenic Area could be divided into three ecological sensitivity sub-areas (high, moderate, and low), three tourism functional sub-areas (restricted development ecotourism, moderate development ecotourism, and mass tourism), and six tourism functional sub-areas (wetland protection, primitive ecological sightseeing, agriculture and pasture tourism, grassland tourism, town tourism, and rural tourism).

Snake River Plain Geothermal Play Fairway Analysis - Phase 1 Raster Files

DOE Data Explorer

John Shervais

2015-10-09

Snake River Plain Play Fairway Analysis - Phase 1 CRS Raster Files. This dataset contains raster files created in ArcGIS. These raster images depict Common Risk Segment (CRS) maps for HEAT, PERMEABILITY, AND SEAL, as well as selected maps of Evidence Layers. These evidence layers consist of either Bayesian krige functions or kernel density functions, and include: (1) HEAT: Heat flow (Bayesian krige map), Heat flow standard error on the krige function (data confidence), volcanic vent distribution as function of age and size, groundwater temperature (equivalue interval and natural breaks bins), and groundwater T standard error. (2) PERMEABILTY: Fault and lineament maps, both as mapped and as kernel density functions, processed for both dilational tendency (TD) and slip tendency (ST), along with data confidence maps for each data type. Data types include mapped surface faults from USGS and Idaho Geological Survey data bases, as well as unpublished mapping; lineations derived from maximum gradients in magnetic, deep gravity, and intermediate depth gravity anomalies. (3) SEAL: Seal maps based on presence and thickness of lacustrine sediments and base of SRP aquifer. Raster size is 2 km. All files generated in ArcGIS.

GEAR: genomic enrichment analysis of regional DNA copy number changes.

PubMed

Kim, Tae-Min; Jung, Yu-Chae; Rhyu, Mun-Gan; Jung, Myeong Ho; Chung, Yeun-Jun

2008-02-01

We developed an algorithm named GEAR (genomic enrichment analysis of regional DNA copy number changes) for functional interpretation of genome-wide DNA copy number changes identified by array-based comparative genomic hybridization. GEAR selects two types of chromosomal alterations with potential biological relevance, i.e. recurrent and phenotype-specific alterations. Then it performs functional enrichment analysis using a priori selected functional gene sets to identify primary and clinical genomic signatures. The genomic signatures identified by GEAR represent functionally coordinated genomic changes, which can provide clues on the underlying molecular mechanisms related to the phenotypes of interest. GEAR can help the identification of key molecular functions that are activated or repressed in the tumor genomes leading to the improved understanding on the tumor biology. GEAR software is available with online manual in the website, http://www.systemsbiology.co.kr/GEAR/.

Lynx web services for annotations and systems analysis of multi-gene disorders.

PubMed

Sulakhe, Dinanath; Taylor, Andrew; Balasubramanian, Sandhya; Feng, Bo; Xie, Bingqing; Börnigen, Daniela; Dave, Utpal J; Foster, Ian T; Gilliam, T Conrad; Maltsev, Natalia

2014-07-01

Lynx is a web-based integrated systems biology platform that supports annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Lynx has integrated multiple classes of biomedical data (genomic, proteomic, pathways, phenotypic, toxicogenomic, contextual and others) from various public databases as well as manually curated data from our group and collaborators (LynxKB). Lynx provides tools for gene list enrichment analysis using multiple functional annotations and network-based gene prioritization. Lynx provides access to the integrated database and the analytical tools via REST based Web Services (http://lynx.ci.uchicago.edu/webservices.html). This comprises data retrieval services for specific functional annotations, services to search across the complete LynxKB (powered by Lucene), and services to access the analytical tools built within the Lynx platform. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Fusing Quantitative Requirements Analysis with Model-based Systems Engineering

NASA Technical Reports Server (NTRS)

Cornford, Steven L.; Feather, Martin S.; Heron, Vance A.; Jenkins, J. Steven

2006-01-01

A vision is presented for fusing quantitative requirements analysis with model-based systems engineering. This vision draws upon and combines emergent themes in the engineering milieu. "Requirements engineering" provides means to explicitly represent requirements (both functional and non-functional) as constraints and preferences on acceptable solutions, and emphasizes early-lifecycle review, analysis and verification of design and development plans. "Design by shopping" emphasizes revealing the space of options available from which to choose (without presuming that all selection criteria have previously been elicited), and provides means to make understandable the range of choices and their ramifications. "Model-based engineering" emphasizes the goal of utilizing a formal representation of all aspects of system design, from development through operations, and provides powerful tool suites that support the practical application of these principles. A first step prototype towards this vision is described, embodying the key capabilities. Illustrations, implications, further challenges and opportunities are outlined.

Multiscale analysis of restoration priorities for marine shoreline planning.

PubMed

Diefenderfer, Heida L; Sobocinski, Kathryn L; Thom, Ronald M; May, Christopher W; Borde, Amy B; Southard, Susan L; Vavrinec, John; Sather, Nichole K

2009-10-01

Planners are being called on to prioritize marine shorelines for conservation status and restoration action. This study documents an approach to determining the management strategy most likely to succeed based on current conditions at local and landscape scales. The conceptual framework based in restoration ecology pairs appropriate restoration strategies with sites based on the likelihood of producing long-term resilience given the condition of ecosystem structures and processes at three scales: the shorezone unit (site), the drift cell reach (nearshore marine landscape), and the watershed (terrestrial landscape). The analysis is structured by a conceptual ecosystem model that identifies anthropogenic impacts on targeted ecosystem functions. A scoring system, weighted by geomorphic class, is applied to available spatial data for indicators of stress and function using geographic information systems. This planning tool augments other approaches to prioritizing restoration, including historical conditions and change analysis and ecosystem valuation.

An advanced probabilistic structural analysis method for implicit performance functions

NASA Technical Reports Server (NTRS)

Wu, Y.-T.; Millwater, H. R.; Cruse, T. A.

1989-01-01

In probabilistic structural analysis, the performance or response functions usually are implicitly defined and must be solved by numerical analysis methods such as finite element methods. In such cases, the most commonly used probabilistic analysis tool is the mean-based, second-moment method which provides only the first two statistical moments. This paper presents a generalized advanced mean value (AMV) method which is capable of establishing the distributions to provide additional information for reliability design. The method requires slightly more computations than the second-moment method but is highly efficient relative to the other alternative methods. In particular, the examples show that the AMV method can be used to solve problems involving non-monotonic functions that result in truncated distributions.

Production Functions for Water Delivery Systems: Analysis and Estimation Using Dual Cost Function and Implicit Price Specifications

NASA Astrophysics Data System (ADS)

Teeples, Ronald; Glyer, David

1987-05-01

Both policy and technical analysis of water delivery systems have been based on cost functions that are inconsistent with or are incomplete representations of the neoclassical production functions of economics. We present a full-featured production function model of water delivery which can be estimated from a multiproduct, dual cost function. The model features implicit prices for own-water inputs and is implemented as a jointly estimated system of input share equations and a translog cost function. Likelihood ratio tests are performed showing that a minimally constrained, full-featured production function is a necessary specification of the water delivery operations in our sample. This, plus the model's highly efficient and economically correct parameter estimates, confirms the usefulness of a production function approach to modeling the economic activities of water delivery systems.

MALDI MS-based Composition Analysis of the Polymerization Reaction of Toluene Diisocyanate (TDI) and Ethylene Glycol (EG).

PubMed

Ahn, Yeong Hee; Lee, Yeon Jung; Kim, Sung Ho

2015-01-01

This study describes an MS-based analysis method for monitoring changes in polymer composition during the polyaddition polymerization reaction of toluene diisocyanate (TDI) and ethylene glycol (EG). The polymerization was monitored as a function of reaction time using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI TOF MS). The resulting series of polymer adducts terminated with various end-functional groups were precisely identified and the relative compositions of those series were estimated. A new MALDI MS data interpretation method was developed, consisting of a peak-resolving algorithm for overlapping peaks in MALDI MS spectra, a retrosynthetic analysis for the generation of reduced unit mass peaks, and a Gaussian fit-based selection of the most prominent polymer series among the reconstructed unit mass peaks. This method of data interpretation avoids errors originating from side reactions due to the presence of trace water in the reaction mixture or MALDI analysis. Quantitative changes in the relative compositions of the resulting polymer products were monitored as a function of reaction time. These results demonstrate that the mass data interpretation method described herein can be a powerful tool for estimating quantitative changes in the compositions of polymer products arising during a polymerization reaction.

[Brain function recovery after prolonged posttraumatic coma].

PubMed

Klimash, A V; Zhanaidarov, Z S

2016-01-01

To explore the characteristics of brain function recovery in patients after prolonged posttraumatic coma and with long-unconscious states. Eighty-seven patients after prolonged posttraumatic coma were followed-up for two years. An analysis of a clinical/neurological picture after a prolonged episode of coma was based on the dynamics of vital functions, neurological status and patient's reactions to external stimuli. Based on the dynamics of the clinical/neurological picture that shows the recovery of functions of the certain brain areas, three stages of brain function recovery after a prolonged episode of coma were singled out: brain stem areas, diencephalic areas and telencephalic areas. These functional/anatomic areas of brain function recovery after prolonged coma were compared to the present classifications.

SU-F-J-94: Development of a Plug-in Based Image Analysis Tool for Integration Into Treatment Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Owen, D; Anderson, C; Mayo, C

Purpose: To extend the functionality of a commercial treatment planning system (TPS) to support (i) direct use of quantitative image-based metrics within treatment plan optimization and (ii) evaluation of dose-functional volume relationships to assist in functional image adaptive radiotherapy. Methods: A script was written that interfaces with a commercial TPS via an Application Programming Interface (API). The script executes a program that performs dose-functional volume analyses. Written in C#, the script reads the dose grid and correlates it with image data on a voxel-by-voxel basis through API extensions that can access registration transforms. A user interface was designed through WinFormsmore » to input parameters and display results. To test the performance of this program, image- and dose-based metrics computed from perfusion SPECT images aligned to the treatment planning CT were generated, validated, and compared. Results: The integration of image analysis information was successfully implemented as a plug-in to a commercial TPS. Perfusion SPECT images were used to validate the calculation and display of image-based metrics as well as dose-intensity metrics and histograms for defined structures on the treatment planning CT. Various biological dose correction models, custom image-based metrics, dose-intensity computations, and dose-intensity histograms were applied to analyze the image-dose profile. Conclusion: It is possible to add image analysis features to commercial TPSs through custom scripting applications. A tool was developed to enable the evaluation of image-intensity-based metrics in the context of functional targeting and avoidance. In addition to providing dose-intensity metrics and histograms that can be easily extracted from a plan database and correlated with outcomes, the system can also be extended to a plug-in optimization system, which can directly use the computed metrics for optimization of post-treatment tumor or normal tissue response models. Supported by NIH - P01 - CA059827.« less

Functional Technology for Individuals with Intellectual Disabilities: Meta-Analysis of Mobile Device-Based Interventions

ERIC Educational Resources Information Center

Kim, Jemma; Kimm, Christina H.

2017-01-01

This study employs a meta-analysis of single-subject design research to investigate the efficacy of mobile device-based interventions for individuals with intellectual disabilities (ID) and to further examine possible variables that may moderate the intervention outcomes. A total of 23 studies, 78 participants, and 140 observed cases that met the…

Functions of Research in Radical Behaviorism for the Further Development of Behavior Analysis

ERIC Educational Resources Information Center

Leigland, Sam

2010-01-01

The experimental analysis of behavior began as an inductively oriented, empirically based scientific field. As the field grew, its distinctive system of science--radical behaviorism--grew with it. The continuing growth of the empirical base of the field has been accompanied by the growth of the literature on radical behaviorism and its…

Simulation of random road microprofile based on specified correlation function

NASA Astrophysics Data System (ADS)

Rykov, S. P.; Rykova, O. A.; Koval, V. S.; Vlasov, V. G.; Fedotov, K. V.

2018-03-01

The paper aims to develop a numerical simulation method and an algorithm for a random microprofile of special roads based on the specified correlation function. The paper used methods of correlation, spectrum and numerical analysis. It proves that the transfer function of the generating filter for known expressions of spectrum input and output filter characteristics can be calculated using a theorem on nonnegative and fractional rational factorization and integral transformation. The model of the random function equivalent of the real road surface microprofile enables us to assess springing system parameters and identify ranges of variations.

Validation of the Early Functional Abilities scale: An assessment of four dimensions in early recovery after traumatic brain injury.

PubMed

Poulsen, Ingrid; Kreiner, Svend; Engberg, Aase W

2018-02-13

The Early Functional Abilities scale assesses the restoration of brain function after brain injury, based on 4 dimensions. The primary objective of this study was to evaluate the validity, objectivity, reliability and measurement precision of the Early Functional Abilities scale by Rasch model item analysis. A secondary objective was to examine the relationship between the Early Functional Abilities scale and the Functional Independence Measurement™, in order to establish the criterion validity of the Early Functional Abilities scale and to compare the sensitivity of measurements using the 2 instruments. The Rasch analysis was based on the assessment of 408 adult patients at admission to sub-acute rehabilitation in Copenhagen, Denmark after traumatic brain injury. The Early Functional Abilities scale provides valid and objective measurement of vegetative (autonomic), facio-oral, sensorimotor and communicative/cognitive functions. Removal of one item from the sensorimotor scale confirmed unidimensionality for each of the 4 subscales, but not for the entire scale. The Early Functional Abilities subscales are sensitive to differences between patients in ranges in which the Functional Independence Measurement™ has a floor effect. The Early Functional Abilities scale assesses the early recovery of important aspects of brain function after traumatic brain injury, but is not unidimensional. We recommend removal of the "standing" item and calculation of summary subscales for the separate dimensions.

Calibration data Analysis Package (CAP): An IDL based widget application for analysis of X-ray calibration data

NASA Astrophysics Data System (ADS)

Vaishali, S.; Narendranath, S.; Sreekumar, P.

An IDL (interactive data language) based widget application developed for the calibration of C1XS (Narendranath et al., 2010) instrument on Chandrayaan-1 is modified to provide a generic package for the analysis of data from x-ray detectors. The package supports files in ascii as well as FITS format. Data can be fitted with a list of inbuilt functions to derive the spectral redistribution function (SRF). We have incorporated functions such as `HYPERMET' (Philips & Marlow 1976) including non Gaussian components in the SRF such as low energy tail, low energy shelf and escape peak. In addition users can incorporate additional models which may be required to model detector specific features. Spectral fits use a routine `mpfit' which uses Leven-Marquardt least squares fitting method. The SRF derived from this tool can be fed into an accompanying program to generate a redistribution matrix file (RMF) compatible with the X-ray spectral analysis package XSPEC. The tool provides a user friendly interface of help to beginners and also provides transparency and advanced features for experts.

Discriminant analysis in wildlife research: Theory and applications

USGS Publications Warehouse

Williams, B.K.; Capen, D.E.

1981-01-01

Discriminant analysis, a method of analyzing grouped multivariate data, is often used in ecological investigations. It has both a predictive and an explanatory function, the former aiming at classification of individuals of unknown group membership. The goal of the latter function is to exhibit group separation by means of linear transforms, and the corresponding method is called canonical analysis. This discussion focuses on the application of canonical analysis in ecology. In order to clarify its meaning, a parametric approach is taken instead of the usual data-based formulation. For certain assumptions the data-based canonical variates are shown to result from maximum likelihood estimation, thus insuring consistency and asymptotic efficiency. The distorting effects of covariance heterogeneity are examined, as are certain difficulties which arise in interpreting the canonical functions. A 'distortion metric' is defined, by means of which distortions resulting from the canonical transformation can be assessed. Several sampling problems which arise in ecological applications are considered. It is concluded that the method may prove valuable for data exploration, but is of limited value as an inferential procedure.

Using Loss Functions for DIF Detection: An Empirical Bayes Approach.

ERIC Educational Resources Information Center

Zwick, Rebecca; Thayer, Dorothy; Lewis, Charles

2000-01-01

Studied a method for flagging differential item functioning (DIF) based on loss functions. Builds on earlier research that led to the development of an empirical Bayes enhancement to the Mantel-Haenszel DIF analysis. Tested the method through simulation and found its performance better than some commonly used DIF classification systems. (SLD)

A personality-based latent class typology of outpatients with major depressive disorder: association with symptomatology, prescription pattern and social function.

PubMed

Hori, Hiroaki; Teraishi, Toshiya; Nagashima, Anna; Koga, Norie; Ota, Miho; Hattori, Kotaro; Kim, Yoshiharu; Higuchi, Teruhiko; Kunugi, Hiroshi

2017-08-01

While major depressive disorder (MDD) is considered to be a heterogeneous disorder, the nature of the heterogeneity remains unclear. Studies have attempted to classify patients with MDD using latent variable techniques, yet the empirical approaches to symptom-based subtyping of MDD have not provided conclusive evidence. Here we aimed to identify homogeneous classes of MDD based on personality traits, using a latent profile analysis. We studied 238 outpatients with DSM-IV MDD recruited from our specialized depression outpatient clinic and assessed their dimensional personality traits with the Temperament and Character Inventory. Latent profile analysis was conducted with 7 dimensions of the Temperament and Character Inventory as indicators. Relationships of the identified classes with symptomatology, prescription pattern, and social function were then examined. The latent profile analysis indicated that a 3-class solution best fit the data. Of the sample, 46.2% was classified into a "neurotic" group characterized by high harm avoidance and low self-directedness; 30.3% into an "adaptive" group characterized by high self-directedness and cooperativeness; and 23.5% into a "socially-detached" group characterized by low reward dependence and cooperativeness and high self-transcendence. The 2 maladaptive groups, namely neurotic and socially-detached groups, demonstrated unique patterns of symptom expression, different classes of psychotropic medication use, and lower social functioning. Generalizability of the findings was limited since our patients were recruited from the specialized depression outpatient clinic. Our personality-based latent profile analysis identified clinically meaningful 3 MDD groups that were markedly different in their personality profiles associated with distinct symptomatology and functioning. Copyright © 2017 Elsevier B.V. All rights reserved.

Sparse representation of whole-brain fMRI signals for identification of functional networks.

PubMed

Lv, Jinglei; Jiang, Xi; Li, Xiang; Zhu, Dajiang; Chen, Hanbo; Zhang, Tuo; Zhang, Shu; Hu, Xintao; Han, Junwei; Huang, Heng; Zhang, Jing; Guo, Lei; Liu, Tianming

2015-02-01

There have been several recent studies that used sparse representation for fMRI signal analysis and activation detection based on the assumption that each voxel's fMRI signal is linearly composed of sparse components. Previous studies have employed sparse coding to model functional networks in various modalities and scales. These prior contributions inspired the exploration of whether/how sparse representation can be used to identify functional networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, alternative methodology of identifying multiple functional networks via sparse representation of whole-brain task-based fMRI signals. Our basic idea is that all fMRI signals within the whole brain of one subject are aggregated into a big data matrix, which is then factorized into an over-complete dictionary basis matrix and a reference weight matrix via an effective online dictionary learning algorithm. Our extensive experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge. Importantly, these well-characterized functional network components are quite reproducible in different brains. In general, our methods offer a novel, effective and unified solution to multiple fMRI data analysis tasks including activation detection, de-activation detection, and functional network identification. Copyright © 2014 Elsevier B.V. All rights reserved.

Guidelines for VCCT-Based Interlaminar Fatigue and Progressive Failure Finite Element Analysis

NASA Technical Reports Server (NTRS)

Deobald, Lyle R.; Mabson, Gerald E.; Engelstad, Steve; Prabhakar, M.; Gurvich, Mark; Seneviratne, Waruna; Perera, Shenal; O'Brien, T. Kevin; Murri, Gretchen; Ratcliffe, James;

Functional neural networks of honesty and dishonesty in children: Evidence from graph theory analysis.

PubMed

Ding, Xiao Pan; Wu, Si Jia; Liu, Jiangang; Fu, Genyue; Lee, Kang

2017-09-21

The present study examined how different brain regions interact with each other during spontaneous honest vs. dishonest communication. More specifically, we took a complex network approach based on the graph-theory to analyze neural response data when children are spontaneously engaged in honest or dishonest acts. Fifty-nine right-handed children between 7 and 12 years of age participated in the study. They lied or told the truth out of their own volition. We found that lying decreased both the global and local efficiencies of children's functional neural network. This finding, for the first time, suggests that lying disrupts the efficiency of children's cortical network functioning. Further, it suggests that the graph theory based network analysis is a viable approach to study the neural development of deception.

The Information Content of Discrete Functions and Their Application in Genetic Data Analysis.

PubMed

Sakhanenko, Nikita A; Kunert-Graf, James; Galas, David J

2017-12-01

The complex of central problems in data analysis consists of three components: (1) detecting the dependence of variables using quantitative measures, (2) defining the significance of these dependence measures, and (3) inferring the functional relationships among dependent variables. We have argued previously that an information theory approach allows separation of the detection problem from the inference of functional form problem. We approach here the third component of inferring functional forms based on information encoded in the functions. We present here a direct method for classifying the functional forms of discrete functions of three variables represented in data sets. Discrete variables are frequently encountered in data analysis, both as the result of inherently categorical variables and from the binning of continuous numerical variables into discrete alphabets of values. The fundamental question of how much information is contained in a given function is answered for these discrete functions, and their surprisingly complex relationships are illustrated. The all-important effect of noise on the inference of function classes is found to be highly heterogeneous and reveals some unexpected patterns. We apply this classification approach to an important area of biological data analysis-that of inference of genetic interactions. Genetic analysis provides a rich source of real and complex biological data analysis problems, and our general methods provide an analytical basis and tools for characterizing genetic problems and for analyzing genetic data. We illustrate the functional description and the classes of a number of common genetic interaction modes and also show how different modes vary widely in their sensitivity to noise.

On One-Dimensional Stretching Functions for Finite-Difference Calculations

NASA Technical Reports Server (NTRS)

Vinokur, M.

1980-01-01

The class of one dimensional stretching function used in finite difference calculations is studied. For solutions containing a highly localized region of rapid variation, simple criteria for a stretching function are derived using a truncation error analysis. These criteria are used to investigate two types of stretching functions. One is an interior stretching function, for which the location and slope of an interior clustering region are specified. The simplest such function satisfying the criteria is found to be one based on the inverse hyperbolic sine. The other type of function is a two sided stretching function, for which the arbitrary slopes at the two ends of the one dimensional interval are specified. The simplest such general function is found to be one based on the inverse tangent. The general two sided function has many applications in the construction of finite difference grids.

Reliability-Based Stability Analysis of Rock Slopes Using Numerical Analysis and Response Surface Method

NASA Astrophysics Data System (ADS)

Dadashzadeh, N.; Duzgun, H. S. B.; Yesiloglu-Gultekin, N.

2017-08-01

While advanced numerical techniques in slope stability analysis are successfully used in deterministic studies, they have so far found limited use in probabilistic analyses due to their high computation cost. The first-order reliability method (FORM) is one of the most efficient probabilistic techniques to perform probabilistic stability analysis by considering the associated uncertainties in the analysis parameters. However, it is not possible to directly use FORM in numerical slope stability evaluations as it requires definition of a limit state performance function. In this study, an integrated methodology for probabilistic numerical modeling of rock slope stability is proposed. The methodology is based on response surface method, where FORM is used to develop an explicit performance function from the results of numerical simulations. The implementation of the proposed methodology is performed by considering a large potential rock wedge in Sumela Monastery, Turkey. The accuracy of the developed performance function to truly represent the limit state surface is evaluated by monitoring the slope behavior. The calculated probability of failure is compared with Monte Carlo simulation (MCS) method. The proposed methodology is found to be 72% more efficient than MCS, while the accuracy is decreased with an error of 24%.

Imperial College near infrared spectroscopy neuroimaging analysis framework.

PubMed

Orihuela-Espina, Felipe; Leff, Daniel R; James, David R C; Darzi, Ara W; Yang, Guang-Zhong

2018-01-01

This paper describes the Imperial College near infrared spectroscopy neuroimaging analysis (ICNNA) software tool for functional near infrared spectroscopy neuroimaging data. ICNNA is a MATLAB-based object-oriented framework encompassing an application programming interface and a graphical user interface. ICNNA incorporates reconstruction based on the modified Beer-Lambert law and basic processing and data validation capabilities. Emphasis is placed on the full experiment rather than individual neuroimages as the central element of analysis. The software offers three types of analyses including classical statistical methods based on comparison of changes in relative concentrations of hemoglobin between the task and baseline periods, graph theory-based metrics of connectivity and, distinctively, an analysis approach based on manifold embedding. This paper presents the different capabilities of ICNNA in its current version.

Locomotive cab design development. Volume 1 : analysis of locomotive cab environment & development of cab design alternatives

DOT National Transportation Integrated Search

1976-10-01

This report presents an analysis of the line haul freight : engineer's working and living environment, the resultant locomotive : cab design and design alternatives. The analysis is based on a : delineation of functional requirements found in current...

Extracting intrinsic functional networks with feature-based group independent component analysis.

PubMed

Calhoun, Vince D; Allen, Elena

2013-04-01

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro-connectome, particularly in the context of data fusion.

The use of copula functions for predictive analysis of correlations between extreme storm tides

NASA Astrophysics Data System (ADS)

Domino, Krzysztof; Błachowicz, Tomasz; Ciupak, Maurycy

2014-11-01

In this paper we present a method used in quantitative description of weakly predictable hydrological, extreme events at inland sea. Investigations for correlations between variations of individual measuring points, employing combined statistical methods, were carried out. As a main tool for this analysis we used a two-dimensional copula function sensitive for correlated extreme effects. Additionally, a new proposed methodology, based on Detrended Fluctuations Analysis (DFA) and Anomalous Diffusion (AD), was used for the prediction of negative and positive auto-correlations and associated optimum choice of copula functions. As a practical example we analysed maximum storm tides data recorded at five spatially separated places at the Baltic Sea. For the analysis we used Gumbel, Clayton, and Frank copula functions and introduced the reversed Clayton copula. The application of our research model is associated with modelling the risk of high storm tides and possible storm flooding.

Parent-Child Relationships and Family Functioning of Children and Youth Discharged from Residential Mental Health Treatment or a Home-Based Alternative

ERIC Educational Resources Information Center

Preyde, Michele; Cameron, Gary; Frensch, Karen; Adams, Gerald

2011-01-01

This report stems from a larger study on the outcomes of children and youth who accessed residential treatment or a home-based alternative. In this report an analysis of family descriptive information, the nature of family relationships, and indicators of family functioning for children and youth who have participated in children's mental health…

Plant functional genomics

NASA Astrophysics Data System (ADS)

Holtorf, Hauke; Guitton, Marie-Christine; Reski, Ralf

2002-04-01

Functional genome analysis of plants has entered the high-throughput stage. The complete genome information from key species such as Arabidopsis thaliana and rice is now available and will further boost the application of a range of new technologies to functional plant gene analysis. To broadly assign functions to unknown genes, different fast and multiparallel approaches are currently used and developed. These new technologies are based on known methods but are adapted and improved to accommodate for comprehensive, large-scale gene analysis, i.e. such techniques are novel in the sense that their design allows researchers to analyse many genes at the same time and at an unprecedented pace. Such methods allow analysis of the different constituents of the cell that help to deduce gene function, namely the transcripts, proteins and metabolites. Similarly the phenotypic variations of entire mutant collections can now be analysed in a much faster and more efficient way than before. The different methodologies have developed to form their own fields within the functional genomics technological platform and are termed transcriptomics, proteomics, metabolomics and phenomics. Gene function, however, cannot solely be inferred by using only one such approach. Rather, it is only by bringing together all the information collected by different functional genomic tools that one will be able to unequivocally assign functions to unknown plant genes. This review focuses on current technical developments and their impact on the field of plant functional genomics. The lower plant Physcomitrella is introduced as a new model system for gene function analysis, owing to its high rate of homologous recombination.

GOATS Image Projection Component

NASA Technical Reports Server (NTRS)

Haber, Benjamin M.; Green, Joseph J.

2011-01-01

When doing mission analysis and design of an imaging system in orbit around the Earth, answering the fundamental question of imaging performance requires an understanding of the image products that will be produced by the imaging system. GOATS software represents a series of MATLAB functions to provide for geometric image projections. Unique features of the software include function modularity, a standard MATLAB interface, easy-to-understand first-principles-based analysis, and the ability to perform geometric image projections of framing type imaging systems. The software modules are created for maximum analysis utility, and can all be used independently for many varied analysis tasks, or used in conjunction with other orbit analysis tools.

Behavior analytic approaches to problem behavior in intellectual disabilities.

PubMed

Hagopian, Louis P; Gregory, Meagan K

2016-03-01

The purpose of the current review is to summarize recent behavior analytic research on problem behavior in individuals with intellectual disabilities. We have focused our review on studies published from 2013 to 2015, but also included earlier studies that were relevant. Behavior analytic research on problem behavior continues to focus on the use and refinement of functional behavioral assessment procedures and function-based interventions. During the review period, a number of studies reported on procedures aimed at making functional analysis procedures more time efficient. Behavioral interventions continue to evolve, and there were several larger scale clinical studies reporting on multiple individuals. There was increased attention on the part of behavioral researchers to develop statistical methods for analysis of within subject data and continued efforts to aggregate findings across studies through evaluative reviews and meta-analyses. Findings support continued utility of functional analysis for guiding individualized interventions and for classifying problem behavior. Modifications designed to make functional analysis more efficient relative to the standard method of functional analysis were reported; however, these require further validation. Larger scale studies on behavioral assessment and treatment procedures provided additional empirical support for effectiveness of these approaches and their sustainability outside controlled clinical settings.

Fish functional traits correlated with environmental variables in a temperate biodiversity hotspot.

PubMed

Keck, Benjamin P; Marion, Zachary H; Martin, Derek J; Kaufman, Jason C; Harden, Carol P; Schwartz, John S; Strange, Richard J

2014-01-01

The global biodiversity crisis has invigorated the search for generalized patterns in most disciplines within the natural sciences. Studies based on organismal functional traits attempt to broaden implications of results by identifying the response of functional traits, instead of taxonomic units, to environmental variables. Determining the functional trait responses enables more direct comparisons with, or predictions for, communities of different taxonomic composition. The North American freshwater fish fauna is both diverse and increasingly imperiled through human mediated disturbances, including climate change. The Tennessee River, USA, contains one of the most diverse assemblages of freshwater fish in North America and has more imperiled species than other rivers, but there has been no trait-based study of community structure in the system. We identified 211 localities in the upper Tennessee River that were sampled by the Tennessee Valley Authority between 2009 and 2011 and compiled fish functional traits for the observed species and environmental variables for each locality. Using fourth corner analysis, we identified significant correlations between many fish functional traits and environmental variables. Functional traits associated with an opportunistic life history strategy were correlated with localities subject to greater land use disturbance and less flow regulation, while functional traits associated with a periodic life history strategy were correlated with localities subject to regular disturbance and regulated flow. These are patterns observed at the continental scale, highlighting the generalizability of trait-based methods. Contrary to studies that found no community structure differences when considering riparian buffer zones, we found that fish functional traits were correlated with different environmental variables between analyses with buffer zones vs. entire catchment area land cover proportions. Using existing databases and fourth corner analysis, our results support the broad application potential for trait-based methods and indicate trait-based methods can detect environmental filtering by riparian zone land cover.

Fish Functional Traits Correlated with Environmental Variables in a Temperate Biodiversity Hotspot

PubMed Central

Keck, Benjamin P.; Marion, Zachary H.; Martin, Derek J.; Kaufman, Jason C.; Harden, Carol P.; Schwartz, John S.; Strange, Richard J.

2014-01-01

The global biodiversity crisis has invigorated the search for generalized patterns in most disciplines within the natural sciences. Studies based on organismal functional traits attempt to broaden implications of results by identifying the response of functional traits, instead of taxonomic units, to environmental variables. Determining the functional trait responses enables more direct comparisons with, or predictions for, communities of different taxonomic composition. The North American freshwater fish fauna is both diverse and increasingly imperiled through human mediated disturbances, including climate change. The Tennessee River, USA, contains one of the most diverse assemblages of freshwater fish in North America and has more imperiled species than other rivers, but there has been no trait-based study of community structure in the system. We identified 211 localities in the upper Tennessee River that were sampled by the Tennessee Valley Authority between 2009 and 2011 and compiled fish functional traits for the observed species and environmental variables for each locality. Using fourth corner analysis, we identified significant correlations between many fish functional traits and environmental variables. Functional traits associated with an opportunistic life history strategy were correlated with localities subject to greater land use disturbance and less flow regulation, while functional traits associated with a periodic life history strategy were correlated with localities subject to regular disturbance and regulated flow. These are patterns observed at the continental scale, highlighting the generalizability of trait-based methods. Contrary to studies that found no community structure differences when considering riparian buffer zones, we found that fish functional traits were correlated with different environmental variables between analyses with buffer zones vs. entire catchment area land cover proportions. Using existing databases and fourth corner analysis, our results support the broad application potential for trait-based methods and indicate trait-based methods can detect environmental filtering by riparian zone land cover. PMID:24676053

WebBio, a web-based management and analysis system for patient data of biological products in hospital.

PubMed

Lu, Ying-Hao; Kuo, Chen-Chun; Huang, Yaw-Bin

2011-08-01

We selected HTML, PHP and JavaScript as the programming languages to build "WebBio", a web-based system for patient data of biological products and used MySQL as database. WebBio is based on the PHP-MySQL suite and is run by Apache server on Linux machine. WebBio provides the functions of data management, searching function and data analysis for 20 kinds of biological products (plasma expanders, human immunoglobulin and hematological products). There are two particular features in WebBio: (1) pharmacists can rapidly find out whose patients used contaminated products for medication safety, and (2) the statistics charts for a specific product can be automatically generated to reduce pharmacist's work loading. WebBio has successfully turned traditional paper work into web-based data management.

Noncoding sequence classification based on wavelet transform analysis: part II

NASA Astrophysics Data System (ADS)

Paredes, O.; Strojnik, M.; Romo-Vázquez, R.; Vélez-Pérez, H.; Ranta, R.; Garcia-Torales, G.; Scholl, M. K.; Morales, J. A.

2017-09-01

DNA sequences in human genome can be divided into the coding and noncoding ones. We hypothesize that the characteristic periodicities of the noncoding sequences are related to their function. We describe the procedure to identify these characteristic periodicities using the wavelet analysis. Our results show that three groups of noncoding sequences, each one with different biological function, may be differentiated by their wavelet coefficients within specific frequency range.

Availability Estimate of a Conceptual ESM System.

DTIC Science & Technology

1979-06-01

affect mission operation.t A functional block level failure modes and effects analysis ( FMEA ) performed on the filter resulted in an assessed failure rate...is based on an FMEA of failures that disable the function (see Appendix A). A further 29 examination of the filter piece-parts reveals that the driver...Digital-to-analog converter DC Direct current DF Direction finding ESM Electronic Support Measures FMEA Failure modes and effects analysis FMPO

Identification and functional analysis of long non-coding RNAs in human and mouse early embryos based on single-cell transcriptome data

PubMed Central

Qiu, Jia-jun; Ren, Zhao-rui; Yan, Jing-bin

2016-01-01

Epigenetics regulations have an important role in fertilization and proper embryonic development, and several human diseases are associated with epigenetic modification disorders, such as Rett syndrome, Beckwith-Wiedemann syndrome and Angelman syndrome. However, the dynamics and functions of long non-coding RNAs (lncRNAs), one type of epigenetic regulators, in human pre-implantation development have not yet been demonstrated. In this study, a comprehensive analysis of human and mouse early-stage embryonic lncRNAs was performed based on public single-cell RNA sequencing data. Expression profile analysis revealed that lncRNAs are expressed in a developmental stage–specific manner during human early-stage embryonic development, whereas a more temporal-specific expression pattern was identified in mouse embryos. Weighted gene co-expression network analysis suggested that lncRNAs involved in human early-stage embryonic development are associated with several important functions and processes, such as oocyte maturation, zygotic genome activation and mitochondrial functions. We also found that the network of lncRNAs involved in zygotic genome activation was highly preservative between human and mouse embryos, whereas in other stages no strong correlation between human and mouse embryo was observed. This study provides insight into the molecular mechanism underlying lncRNA involvement in human pre-implantation embryonic development. PMID:27542205

Resting-State Brain Activity in Adult Males Who Stutter

PubMed Central

Zhu, Chaozhe; Wang, Liang; Yan, Qian; Lin, Chunlan; Yu, Chunshui

2012-01-01

Although developmental stuttering has been extensively studied with structural and task-based functional magnetic resonance imaging (fMRI), few studies have focused on resting-state brain activity in this disorder. We investigated resting-state brain activity of stuttering subjects by analyzing the amplitude of low-frequency fluctuation (ALFF), region of interest (ROI)-based functional connectivity (FC) and independent component analysis (ICA)-based FC. Forty-four adult males with developmental stuttering and 46 age-matched fluent male controls were scanned using resting-state fMRI. ALFF, ROI-based FCs and ICA-based FCs were compared between male stuttering subjects and fluent controls in a voxel-wise manner. Compared with fluent controls, stuttering subjects showed increased ALFF in left brain areas related to speech motor and auditory functions and bilateral prefrontal cortices related to cognitive control. However, stuttering subjects showed decreased ALFF in the left posterior language reception area and bilateral non-speech motor areas. ROI-based FC analysis revealed decreased FC between the posterior language area involved in the perception and decoding of sensory information and anterior brain area involved in the initiation of speech motor function, as well as increased FC within anterior or posterior speech- and language-associated areas and between the prefrontal areas and default-mode network (DMN) in stuttering subjects. ICA showed that stuttering subjects had decreased FC in the DMN and increased FC in the sensorimotor network. Our findings support the concept that stuttering subjects have deficits in multiple functional systems (motor, language, auditory and DMN) and in the connections between them. PMID:22276215

Comparing ab initio density-functional and wave function theories: the impact of correlation on the electronic density and the role of the correlation potential.

PubMed

Grabowski, Ireneusz; Teale, Andrew M; Śmiga, Szymon; Bartlett, Rodney J

2011-09-21

The framework of ab initio density-functional theory (DFT) has been introduced as a way to provide a seamless connection between the Kohn-Sham (KS) formulation of DFT and wave-function based ab initio approaches [R. J. Bartlett, I. Grabowski, S. Hirata, and S. Ivanov, J. Chem. Phys. 122, 034104 (2005)]. Recently, an analysis of the impact of dynamical correlation effects on the density of the neon atom was presented [K. Jankowski, K. Nowakowski, I. Grabowski, and J. Wasilewski, J. Chem. Phys. 130, 164102 (2009)], contrasting the behaviour for a variety of standard density functionals with that of ab initio approaches based on second-order Møller-Plesset (MP2) and coupled cluster theories at the singles-doubles (CCSD) and singles-doubles perturbative triples [CCSD(T)] levels. In the present work, we consider ab initio density functionals based on second-order many-body perturbation theory and coupled cluster perturbation theory in a similar manner, for a range of small atomic and molecular systems. For comparison, we also consider results obtained from MP2, CCSD, and CCSD(T) calculations. In addition to this density based analysis, we determine the KS correlation potentials corresponding to these densities and compare them with those obtained for a range of ab initio density functionals via the optimized effective potential method. The correlation energies, densities, and potentials calculated using ab initio DFT display a similar systematic behaviour to those derived from electronic densities calculated using ab initio wave function theories. In contrast, typical explicit density functionals for the correlation energy, such as VWN5 and LYP, do not show behaviour consistent with this picture of dynamical correlation, although they may provide some degree of correction for already erroneous explicitly density-dependent exchange-only functionals. The results presented here using orbital dependent ab initio density functionals show that they provide a treatment of exchange and correlation contributions within the KS framework that is more consistent with traditional ab initio wave function based methods.

Interoperability-oriented Integration of Failure Knowledge into Functional Knowledge and Knowledge Transformation based on Concepts Mapping

NASA Astrophysics Data System (ADS)

Koji, Yusuke; Kitamura, Yoshinobu; Kato, Yoshikiyo; Tsutsui, Yoshio; Mizoguchi, Riichiro

In conceptual design, it is important to develop functional structures which reflect the rich experience in the knowledge from previous design failures. Especially, if a designer learns possible abnormal behaviors from a previous design failure, he or she can add an additional function which prevents such abnormal behaviors and faults. To do this, it is a crucial issue to share such knowledge about possible faulty phenomena and how to cope with them. In fact, a part of such knowledge is described in FMEA (Failure Mode and Effect Analysis) sheets, function structure models for systematic design and fault trees for FTA (Fault Tree Analysis).

Network analysis of mesoscale optical recordings to assess regional, functional connectivity.

PubMed

Lim, Diana H; LeDue, Jeffrey M; Murphy, Timothy H

2015-10-01

With modern optical imaging methods, it is possible to map structural and functional connectivity. Optical imaging studies that aim to describe large-scale neural connectivity often need to handle large and complex datasets. In order to interpret these datasets, new methods for analyzing structural and functional connectivity are being developed. Recently, network analysis, based on graph theory, has been used to describe and quantify brain connectivity in both experimental and clinical studies. We outline how to apply regional, functional network analysis to mesoscale optical imaging using voltage-sensitive-dye imaging and channelrhodopsin-2 stimulation in a mouse model. We include links to sample datasets and an analysis script. The analyses we employ can be applied to other types of fluorescence wide-field imaging, including genetically encoded calcium indicators, to assess network properties. We discuss the benefits and limitations of using network analysis for interpreting optical imaging data and define network properties that may be used to compare across preparations or other manipulations such as animal models of disease.

Recent advances in proteomics of cereals.

PubMed

Bansal, Monika; Sharma, Madhu; Kanwar, Priyanka; Goyal, Aakash

Cereals contribute a major part of human nutrition and are considered as an integral source of energy for human diets. With genomic databases already available in cereals such as rice, wheat, barley, and maize, the focus has now moved to proteome analysis. Proteomics studies involve the development of appropriate databases based on developing suitable separation and purification protocols, identification of protein functions, and can confirm their functional networks based on already available data from other sources. Tremendous progress has been made in the past decade in generating huge data-sets for covering interactions among proteins, protein composition of various organs and organelles, quantitative and qualitative analysis of proteins, and to characterize their modulation during plant development, biotic, and abiotic stresses. Proteomics platforms have been used to identify and improve our understanding of various metabolic pathways. This article gives a brief review of efforts made by different research groups on comparative descriptive and functional analysis of proteomics applications achieved in the cereal science so far.

Exercise for lower limb osteoarthritis: systematic review incorporating trial sequential analysis and network meta-analysis.

PubMed

Uthman, Olalekan A; van der Windt, Danielle A; Jordan, Joanne L; Dziedzic, Krysia S; Healey, Emma L; Peat, George M; Foster, Nadine E

2014-11-01

Which types of exercise intervention are most effective in relieving pain and improving function in people with lower limb osteoarthritis? As of 2002 sufficient evidence had accumulated to show significant benefit of exercise over no exercise. An approach combining exercises to increase strength, flexibility, and aerobic capacity is most likely to be effective for relieving pain and improving function. Current international guidelines recommend therapeutic exercise (land or water based) as "core" and effective management of osteoarthritis. Evidence from this first network meta-analysis, largely based on studies in knee osteoarthritis, indicates that an intervention combining strengthening exercises with flexibility and aerobic exercise is most likely to improve outcomes of pain and function. Further trials of exercise versus no exercise are unlikely to overturn this positive result. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

PRISM: Processing routines in IDL for spectroscopic measurements (installation manual and user's guide, version 1.0)

USGS Publications Warehouse

Kokaly, Raymond F.

2011-01-01

This report describes procedures for installing and using the U.S. Geological Survey Processing Routines in IDL for Spectroscopic Measurements (PRISM) software. PRISM provides a framework to conduct spectroscopic analysis of measurements made using laboratory, field, airborne, and space-based spectrometers. Using PRISM functions, the user can compare the spectra of materials of unknown composition with reference spectra of known materials. This spectroscopic analysis allows the composition of the material to be identified and characterized. Among its other functions, PRISM contains routines for the storage of spectra in database files, import/export of ENVI spectral libraries, importation of field spectra, correction of spectra to absolute reflectance, arithmetic operations on spectra, interactive continuum removal and comparison of spectral features, correction of imaging spectrometer data to ground-calibrated reflectance, and identification and mapping of materials using spectral feature-based analysis of reflectance data. This report provides step-by-step instructions for installing the PRISM software and running its functions.

Functional network-based statistics in depression: Theory of mind subnetwork and importance of parietal region.

PubMed

Lai, Chien-Han; Wu, Yu-Te; Hou, Yuh-Ming

2017-08-01

The functional network analysis of whole brain is an emerging field for research in depression. We initiated this study to investigate which subnetwork is significantly altered within the functional connectome in major depressive disorder (MDD). The study enrolled 52 first-episode medication-naïve patients with MDD and 40 controls for functional network analysis. All participants received the resting-state functional imaging using a 3-Tesla magnetic resonance scanner. After preprocessing, we calculated the connectivity matrix of functional connectivity in whole brain for each subject. The network-based statistics of connectome was used to perform group comparisons between patients and controls. The correlations between functional connectivity and clinical parameters were also performed. MDD patients had significant alterations in the network involving "theory of mind" regions, such as the left precentral gyrus, left angular gyrus, bilateral rolandic operculums and left inferior frontal gyrus. The center node of significant network was the left angular gyrus. No significant correlations of functional connectivity within the subnetwork and clinical parameters were noted. Functional connectivity of "theory of mind" subnetwork may be the core issue for pathophysiology in MDD. In addition, the center role of parietal region should be emphasized in future study. Copyright © 2017. Published by Elsevier B.V.

On one-dimensional stretching functions for finite-difference calculations. [computational fluid dynamics

NASA Technical Reports Server (NTRS)

Vinokur, M.

1983-01-01

The class of one-dimensional stretching functions used in finite-difference calculations is studied. For solutions containing a highly localized region of rapid variation, simple criteria for a stretching function are derived using a truncation error analysis. These criteria are used to investigate two types of stretching functions. One an interior stretching function, for which the location and slope of an interior clustering region are specified. The simplest such function satisfying the criteria is found to be one based on the inverse hyperbolic sine. The other type of function is a two-sided stretching function, for which the arbitrary slopes at the two ends of the one-dimensional interval are specified. The simplest such general function is found to be one based on the inverse tangent. Previously announced in STAR as N80-25055

Impact of different NWM-derived mapping functions on VLBI and GPS analysis

NASA Astrophysics Data System (ADS)

Nikolaidou, Thalia; Balidakis, Kyriakos; Nievinski, Felipe; Santos, Marcelo; Schuh, Harald

2018-06-01

In recent years, numerical weather models have shown the potential to provide a good representation of the electrically neutral atmosphere. This fact has been exploited for the modeling of space geodetic observations. The Vienna Mapping Functions 1 (VMF1) are the NWM-based model recommended by the latest IERS Conventions. The VMF1 are being produced 6 hourly based on the European Centre for Medium-Range Weather Forecasts operational model. UNB-VMF1 provide meteorological parameters aiding neutral atmosphere modeling for VLBI and GNSS, based on the same concept but utilizing the Canadian Meteorological Centre model. This study presents comparisons between the VMF1 and the UNB-VMF1 in both delay and position domains, using global networks of VLBI and GPS stations. It is shown that the zenith delays agree better than 3.5 mm (hydrostatic) and 20 mm (wet) which implies an equivalent predicted height error of less than 2 mm. In the position domain and VLBI analysis, comparison of the weighted root-mean-square error (wrms) of the height component showed a maximum difference of 1.7 mm. For 48% of the stations, the use of VMF1 reduced the height wrms of the stations by 2.6% on average compared to a respective reduction of 1.7% for 41% of the stations employing the UNB-VMF1. For the subset of VLBI stations participating in a large number of sessions, neither mapping function outranked the other. GPS analysis using Precise Point Positioning had a sub-mm respective difference, while the wrms of the individual solutions had a maximum value of 12 mm for the 1-year-long analysis. A clear advantage of one NWM over the other was not shown, and the statistics proved that the two mapping functions yield equal results in geodetic analysis.

Wastewater-Based Epidemiology of Stimulant Drugs: Functional Data Analysis Compared to Traditional Statistical Methods.

PubMed

Salvatore, Stefania; Bramness, Jørgen Gustav; Reid, Malcolm J; Thomas, Kevin Victor; Harman, Christopher; Røislien, Jo

2015-01-01

Wastewater-based epidemiology (WBE) is a new methodology for estimating the drug load in a population. Simple summary statistics and specification tests have typically been used to analyze WBE data, comparing differences between weekday and weekend loads. Such standard statistical methods may, however, overlook important nuanced information in the data. In this study, we apply functional data analysis (FDA) to WBE data and compare the results to those obtained from more traditional summary measures. We analysed temporal WBE data from 42 European cities, using sewage samples collected daily for one week in March 2013. For each city, the main temporal features of two selected drugs were extracted using functional principal component (FPC) analysis, along with simpler measures such as the area under the curve (AUC). The individual cities' scores on each of the temporal FPCs were then used as outcome variables in multiple linear regression analysis with various city and country characteristics as predictors. The results were compared to those of functional analysis of variance (FANOVA). The three first FPCs explained more than 99% of the temporal variation. The first component (FPC1) represented the level of the drug load, while the second and third temporal components represented the level and the timing of a weekend peak. AUC was highly correlated with FPC1, but other temporal characteristic were not captured by the simple summary measures. FANOVA was less flexible than the FPCA-based regression, and even showed concordance results. Geographical location was the main predictor for the general level of the drug load. FDA of WBE data extracts more detailed information about drug load patterns during the week which are not identified by more traditional statistical methods. Results also suggest that regression based on FPC results is a valuable addition to FANOVA for estimating associations between temporal patterns and covariate information.

The Big Issue: Command and Combat in the Information Age

DTIC Science & Technology

2003-02-01

a new construct might emerge based on sensing forces, strike forces and manoeuvre forces.5 Whatever the outcome , an agile and razor-sharp command...wide range of mission support functions carried out in the home base – including intelligence, legal support, course of action analysis and operational... analysis as well as rear- based logistics. At the strategic level, once a major expeditionary operation is underway, rear operations might also embrace

CM-DataONE: A Framework for collaborative analysis of climate model output

NASA Astrophysics Data System (ADS)

Xu, Hao; Bai, Yuqi; Li, Sha; Dong, Wenhao; Huang, Wenyu; Xu, Shiming; Lin, Yanluan; Wang, Bin

2015-04-01

CM-DataONE is a distributed collaborative analysis framework for climate model data which aims to break through the data access barriers of increasing file size and to accelerate research process. As data size involved in project such as the fifth Coupled Model Intercomparison Project (CMIP5) has reached petabytes, conventional methods for analysis and diagnosis of model outputs have been rather time-consuming and redundant. CM-DataONE is developed for data publishers and researchers from relevant areas. It can enable easy access to distributed data and provide extensible analysis functions based on tools such as NCAR Command Language, NetCDF Operators (NCO) and Climate Data Operators (CDO). CM-DataONE can be easily installed, configured, and maintained. The main web application has two separate parts which communicate with each other through APIs based on HTTP protocol. The analytic server is designed to be installed in each data node while a data portal can be configured anywhere and connect to a nearest node. Functions such as data query, analytic task submission, status monitoring, visualization and product downloading are provided to end users by data portal. Data conform to CMIP5 Model Output Format in each peer node can be scanned by the server and mapped to a global information database. A scheduler included in the server is responsible for task decomposition, distribution and consolidation. Analysis functions are always executed where data locate. Analysis function package included in the server has provided commonly used functions such as EOF analysis, trend analysis and time series. Functions are coupled with data by XML descriptions and can be easily extended. Various types of results can be obtained by users for further studies. This framework has significantly decreased the amount of data to be transmitted and improved efficiency in model intercomparison jobs by supporting online analysis and multi-node collaboration. To end users, data query is therefore accelerated and the size of data to be downloaded is reduced. Methodology can be easily shared among scientists, avoiding unnecessary replication. Currently, a prototype of CM-DataONE has been deployed on two data nodes of Tsinghua University.

An Investigation of Document Partitions.

ERIC Educational Resources Information Center

Shaw, W. M., Jr.

1986-01-01

Empirical significance of document partitions is investigated as a function of index term-weight and similarity thresholds. Results show the same empirically preferred partitions can be detected by two independent strategies: an analysis of cluster-based retrieval analysis and an analysis of regularities in the underlying structure of the document…

Creativity and the default network: A functional connectivity analysis of the creative brain at rest.

PubMed

Beaty, Roger E; Benedek, Mathias; Wilkins, Robin W; Jauk, Emanuel; Fink, Andreas; Silvia, Paul J; Hodges, Donald A; Koschutnig, Karl; Neubauer, Aljoscha C

2014-11-01

The present research used resting-state functional magnetic resonance imaging (fMRI) to examine whether the ability to generate creative ideas corresponds to differences in the intrinsic organization of functional networks in the brain. We examined the functional connectivity between regions commonly implicated in neuroimaging studies of divergent thinking, including the inferior prefrontal cortex and the core hubs of the default network. Participants were prescreened on a battery of divergent thinking tests and assigned to high- and low-creative groups based on task performance. Seed-based functional connectivity analysis revealed greater connectivity between the left inferior frontal gyrus (IFG) and the entire default mode network in the high-creative group. The right IFG also showed greater functional connectivity with bilateral inferior parietal cortex and the left dorsolateral prefrontal cortex in the high-creative group. The results suggest that the ability to generate creative ideas is characterized by increased functional connectivity between the inferior prefrontal cortex and the default network, pointing to a greater cooperation between brain regions associated with cognitive control and low-level imaginative processes. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Protein-binding aptamer assisted signal amplification for the detection of influenza A (H1N1) DNA sequences based on quantum dot fluorescence polarization analysis.

PubMed

Zhang, Juanni; Tian, Jianniao; He, Yanlong; Chen, Sheng; Jiang, Yixuan; Zhao, Yanchun; Zhao, Shulin

2013-09-07

We report a fluorescence polarization platform for H1N1 detection based on the construction of a DNA functional QD fluorescence polarization probe and a bi-functional protein binding aptamer (Apt-DNA). The assay has a linear range from 10 nM to 100 nM with a detection limit of 3.45 nM and is selective over the mismatched bases.

Possible alternative to European Pharmacopoeia's method of analysis Test for Fc Function of Immunoglobulin (2.7.9) by using tetanus toxoid as antigen.

PubMed

Perez-del-Pulgar, S; Lopez, M; Gensana, M; Jorquera, J I

2006-08-01

Preparations of intravenous immunoglobulins must keep functional integrity throughout the purification process. In order to assess Fc fragment functionality, the European Pharmacopoeia proposes the Test for Fc function of immunoglobulin (2.7.9), which is based on a rubella antigen of high titre. Sometimes, such antigen is difficult to obtain. In the present study, we develop the same assay using tetanus toxoid instead of rubella antigen, adapting the procedure for the use of tetanus toxoid. The comparison between rubella-based and tetanus-based assays showed that the slopes of the haemolysis curves were higher if red blood cells had been sensitised with the rubella antigen than with tetanus toxoid. Nonetheless, the tetanus-based assay gave satisfactory results and it could be a good alternative antigen target.

Software ion scan functions in analysis of glycomic and lipidomic MS/MS datasets.

PubMed

Haramija, Marko

2018-03-01

Hardware ion scan functions unique to tandem mass spectrometry (MS/MS) mode of data acquisition, such as precursor ion scan (PIS) and neutral loss scan (NLS), are important for selective extraction of key structural data from complex MS/MS spectra. However, their software counterparts, software ion scan (SIS) functions, are still not regularly available. Software ion scan functions can be easily coded for additional functionalities, such as software multiple precursor ion scan, software no ion scan, and software variable ion scan functions. These are often necessary, since they allow more efficient analysis of complex MS/MS datasets, often encountered in glycomics and lipidomics. Software ion scan functions can be easily coded by using modern script languages and can be independent of instrument manufacturer. Here we demonstrate the utility of SIS functions on a medium-size glycomic MS/MS dataset. Knowledge of sample properties, as well as of diagnostic and conditional diagnostic ions crucial for data analysis, was needed. Based on the tables constructed with the output data from the SIS functions performed, a detailed analysis of a complex MS/MS glycomic dataset could be carried out in a quick, accurate, and efficient manner. Glycomic research is progressing slowly, and with respect to the MS experiments, one of the key obstacles for moving forward is the lack of appropriate bioinformatic tools necessary for fast analysis of glycomic MS/MS datasets. Adding novel SIS functionalities to the glycomic MS/MS toolbox has a potential to significantly speed up the glycomic data analysis process. Similar tools are useful for analysis of lipidomic MS/MS datasets as well, as will be discussed briefly. Copyright © 2017 John Wiley & Sons, Ltd.

Model-driven development of covariances for spatiotemporal environmental health assessment.

PubMed

Kolovos, Alexander; Angulo, José Miguel; Modis, Konstantinos; Papantonopoulos, George; Wang, Jin-Feng; Christakos, George

2013-01-01

Known conceptual and technical limitations of mainstream environmental health data analysis have directed research to new avenues. The goal is to deal more efficiently with the inherent uncertainty and composite space-time heterogeneity of key attributes, account for multi-sourced knowledge bases (health models, survey data, empirical relationships etc.), and generate more accurate predictions across space-time. Based on a versatile, knowledge synthesis methodological framework, we introduce new space-time covariance functions built by integrating epidemic propagation models and we apply them in the analysis of existing flu datasets. Within the knowledge synthesis framework, the Bayesian maximum entropy theory is our method of choice for the spatiotemporal prediction of the ratio of new infectives (RNI) for a case study of flu in France. The space-time analysis is based on observations during a period of 15 weeks in 1998-1999. We present general features of the proposed covariance functions, and use these functions to explore the composite space-time RNI dependency. We then implement the findings to generate sufficiently detailed and informative maps of the RNI patterns across space and time. The predicted distributions of RNI suggest substantive relationships in accordance with the typical physiographic and climatologic features of the country.

Binary partition tree analysis based on region evolution and its application to tree simplification.

PubMed

Lu, Huihai; Woods, John C; Ghanbari, Mohammed

2007-04-01

Pyramid image representations via tree structures are recognized methods for region-based image analysis. Binary partition trees can be applied which document the merging process with small details found at the bottom levels and larger ones close to the root. Hindsight of the merging process is stored within the tree structure and provides the change histories of an image property from the leaf to the root node. In this work, the change histories are modelled by evolvement functions and their second order statistics are analyzed by using a knee function. Knee values show the reluctancy of each merge. We have systematically formulated these findings to provide a novel framework for binary partition tree analysis, where tree simplification is demonstrated. Based on an evolvement function, for each upward path in a tree, the tree node associated with the first reluctant merge is considered as a pruning candidate. The result is a simplified version providing a reduced solution space and still complying with the definition of a binary tree. The experiments show that image details are preserved whilst the number of nodes is dramatically reduced. An image filtering tool also results which preserves object boundaries and has applications for segmentation.

Robustness of Representative Signals Relative to Data Loss Using Atlas-Based Parcellations.

PubMed

Gajdoš, Martin; Výtvarová, Eva; Fousek, Jan; Lamoš, Martin; Mikl, Michal

2018-04-24

Parcellation-based approaches are an important part of functional magnetic resonance imaging data analysis. They are a necessary processing step for sorting data in structurally or functionally homogenous regions. Real functional magnetic resonance imaging datasets usually do not cover the atlas template completely; they are often spatially constrained due to the physical limitations of MR sequence settings, the inter-individual variability in brain shape, etc. When using a parcellation template, many regions are not completely covered by actual data. This paper addresses the issue of the area coverage required in real data in order to reliably estimate the representative signal and the influence of this kind of data loss on network analysis metrics. We demonstrate this issue on four datasets using four different widely used parcellation templates. We used two erosion approaches to simulate data loss on the whole-brain level and the ROI-specific level. Our results show that changes in ROI coverage have a systematic influence on network measures. Based on the results of our analysis, we recommend controlling the ROI coverage and retaining at least 60% of the area in order to ensure at least 80% of explained variance of the original signal.

System diagnostics using qualitative analysis and component functional classification

DOEpatents

Reifman, J.; Wei, T.Y.C.

1993-11-23

A method for detecting and identifying faulty component candidates during off-normal operations of nuclear power plants involves the qualitative analysis of macroscopic imbalances in the conservation equations of mass, energy and momentum in thermal-hydraulic control volumes associated with one or more plant components and the functional classification of components. The qualitative analysis of mass and energy is performed through the associated equations of state, while imbalances in momentum are obtained by tracking mass flow rates which are incorporated into a first knowledge base. The plant components are functionally classified, according to their type, as sources or sinks of mass, energy and momentum, depending upon which of the three balance equations is most strongly affected by a faulty component which is incorporated into a second knowledge base. Information describing the connections among the components of the system forms a third knowledge base. The method is particularly adapted for use in a diagnostic expert system to detect and identify faulty component candidates in the presence of component failures and is not limited to use in a nuclear power plant, but may be used with virtually any type of thermal-hydraulic operating system. 5 figures.

System diagnostics using qualitative analysis and component functional classification

DOEpatents

Reifman, Jaques; Wei, Thomas Y. C.

1993-01-01

A method for detecting and identifying faulty component candidates during off-normal operations of nuclear power plants involves the qualitative analysis of macroscopic imbalances in the conservation equations of mass, energy and momentum in thermal-hydraulic control volumes associated with one or more plant components and the functional classification of components. The qualitative analysis of mass and energy is performed through the associated equations of state, while imbalances in momentum are obtained by tracking mass flow rates which are incorporated into a first knowledge base. The plant components are functionally classified, according to their type, as sources or sinks of mass, energy and momentum, depending upon which of the three balance equations is most strongly affected by a faulty component which is incorporated into a second knowledge base. Information describing the connections among the components of the system forms a third knowledge base. The method is particularly adapted for use in a diagnostic expert system to detect and identify faulty component candidates in the presence of component failures and is not limited to use in a nuclear power plant, but may be used with virtually any type of thermal-hydraulic operating system.

Parametric Model Based On Imputations Techniques for Partly Interval Censored Data

NASA Astrophysics Data System (ADS)

Zyoud, Abdallah; Elfaki, F. A. M.; Hrairi, Meftah

2017-12-01

The term ‘survival analysis’ has been used in a broad sense to describe collection of statistical procedures for data analysis. In this case, outcome variable of interest is time until an event occurs where the time to failure of a specific experimental unit might be censored which can be right, left, interval, and Partly Interval Censored data (PIC). In this paper, analysis of this model was conducted based on parametric Cox model via PIC data. Moreover, several imputation techniques were used, which are: midpoint, left & right point, random, mean, and median. Maximum likelihood estimate was considered to obtain the estimated survival function. These estimations were then compared with the existing model, such as: Turnbull and Cox model based on clinical trial data (breast cancer data), for which it showed the validity of the proposed model. Result of data set indicated that the parametric of Cox model proved to be more superior in terms of estimation of survival functions, likelihood ratio tests, and their P-values. Moreover, based on imputation techniques; the midpoint, random, mean, and median showed better results with respect to the estimation of survival function.

Incorporating "motivation" into the functional analysis of challenging behavior: on the interactive and integrative potential of the motivating operation.

PubMed

Langthorne, Paul; McGill, Peter; O'Reilly, Mark

2007-07-01

Sensitivity theory attempts to account for the variability often observed in challenging behavior by recourse to the "aberrant motivation" of people with intellectual and developmental disabilities. In this article, we suggest that a functional analysis based on environmental (challenging environments) and biological (challenging needs) motivating operations provides a more parsimonious and empirically grounded account of challenging behavior than that proposed by sensitivity theory. It is argued that the concept of the motivating operation provides a means of integrating diverse strands of research without the undue inference of mentalistic constructs. An integrated model of challenging behavior is proposed, one that remains compatible with the central tenets of functional analysis.

Distinctions between organic brain syndrome and functional psychiatric disorders: based on the geriatric mental state interview.

PubMed

Fleiss, J; Gurland, B; Roche, P D

1976-01-01

Discriminant function analysis was employed to study the ability of the Geriatric Mental Status interview to distinguish between patients diagnosed by the project as having an organic brain syndrome or a functional psychiatric disorder. In both New York and London, patients with organic brain syndrome scored significantly higher (p less than 0.05) than those with functional disorders on the factors of impaired memory, disorientation and incomprehensibility and significantly lower on the factors of depression and somatic concerns. Discriminant functions calculated from data on the New York and London patients separately significantly distinguished not only the patients on whom the functions were based but the patients in the other sample as well.

Rational functional representation of flap noise spectra including correction for reflection effects

NASA Technical Reports Server (NTRS)

Miles, J. H.

1974-01-01

A rational function is presented for the acoustic spectra generated by deflection of engine exhaust jets for under-the-wing and over-the-wing versions of externally blown flaps. The functional representation is intended to provide a means for compact storage of data and for data analysis. The expressions are based on Fourier transform functions for the Strouhal normalized pressure spectral density, and on a correction for reflection effects based on Thomas' (1969) N-independent-source model extended by use of a reflected ray transfer function. Curve fit comparisons are presented for blown-flap data taken from turbofan engine tests and from large-scale cold-flow model tests. Application of the rational function to scrubbing noise theory is also indicated.

Computational approaches for drug discovery.

PubMed

Hung, Che-Lun; Chen, Chi-Chun

2014-09-01

Cellular proteins are the mediators of multiple organism functions being involved in physiological mechanisms and disease. By discovering lead compounds that affect the function of target proteins, the target diseases or physiological mechanisms can be modulated. Based on knowledge of the ligand-receptor interaction, the chemical structures of leads can be modified to improve efficacy, selectivity and reduce side effects. One rational drug design technology, which enables drug discovery based on knowledge of target structures, functional properties and mechanisms, is computer-aided drug design (CADD). The application of CADD can be cost-effective using experiments to compare predicted and actual drug activity, the results from which can used iteratively to improve compound properties. The two major CADD-based approaches are structure-based drug design, where protein structures are required, and ligand-based drug design, where ligand and ligand activities can be used to design compounds interacting with the protein structure. Approaches in structure-based drug design include docking, de novo design, fragment-based drug discovery and structure-based pharmacophore modeling. Approaches in ligand-based drug design include quantitative structure-affinity relationship and pharmacophore modeling based on ligand properties. Based on whether the structure of the receptor and its interaction with the ligand are known, different design strategies can be seed. After lead compounds are generated, the rule of five can be used to assess whether these have drug-like properties. Several quality validation methods, such as cost function analysis, Fisher's cross-validation analysis and goodness of hit test, can be used to estimate the metrics of different drug design strategies. To further improve CADD performance, multi-computers and graphics processing units may be applied to reduce costs. © 2014 Wiley Periodicals, Inc.

Detecting coupled collective motions in protein by independent subspace analysis

NASA Astrophysics Data System (ADS)

Sakuraba, Shun; Joti, Yasumasa; Kitao, Akio

2010-11-01

Protein dynamics evolves in a high-dimensional space, comprising aharmonic, strongly correlated motional modes. Such correlation often plays an important role in analyzing protein function. In order to identify significantly correlated collective motions, here we employ independent subspace analysis based on the subspace joint approximate diagonalization of eigenmatrices algorithm for the analysis of molecular dynamics (MD) simulation trajectories. From the 100 ns MD simulation of T4 lysozyme, we extract several independent subspaces in each of which collective modes are significantly correlated, and identify the other modes as independent. This method successfully detects the modes along which long-tailed non-Gaussian probability distributions are obtained. Based on the time cross-correlation analysis, we identified a series of events among domain motions and more localized motions in the protein, indicating the connection between the functionally relevant phenomena which have been independently revealed by experiments.

Sensitivity analysis, approximate analysis, and design optimization for internal and external viscous flows

NASA Technical Reports Server (NTRS)

Taylor, Arthur C., III; Hou, Gene W.; Korivi, Vamshi M.

1991-01-01

A gradient-based design optimization strategy for practical aerodynamic design applications is presented, which uses the 2D thin-layer Navier-Stokes equations. The strategy is based on the classic idea of constructing different modules for performing the major tasks such as function evaluation, function approximation and sensitivity analysis, mesh regeneration, and grid sensitivity analysis, all driven and controlled by a general-purpose design optimization program. The accuracy of aerodynamic shape sensitivity derivatives is validated on two viscous test problems: internal flow through a double-throat nozzle and external flow over a NACA 4-digit airfoil. A significant improvement in aerodynamic performance has been achieved in both cases. Particular attention is given to a consistent treatment of the boundary conditions in the calculation of the aerodynamic sensitivity derivatives for the classic problems of external flow over an isolated lifting airfoil on 'C' or 'O' meshes.

[Design and Analysis of CT High-speed Data Transmission Rotating Connector Ring System Retaining Ring].

PubMed

Pan, Li; Cao, Jujiang; Liu, Min; Fu, Weiwei

2017-11-30

High speed data transmission rotating connector system for signal high-speed transmission used in the fixed end and rotating end, it is one of the core component in the CT system. This paper involves structure design and analysis of the retaining ring in the CT high speed data transmission rotating connector system based on the principle of off-axis free space optical transmission. According to the problem of the actual engineering application of space limitations, optical fiber fixed and collimator installation location, we designed the structure of the retaining ring. Using the static analysis function of ANSYS Workbench, it verifies rationality and safety of the strength of retaining ring structure. And based on modal analysis function of ANSYS Workbench, it evaluates the effect of the retaining ring on the stability of the system date transmission, and provides theoretical basis for the feasibility of the structure in practical application.

Template based rotation: A method for functional connectivity analysis with a priori templates☆

PubMed Central

Schultz, Aaron P.; Chhatwal, Jasmeer P.; Huijbers, Willem; Hedden, Trey; van Dijk, Koene R.A.; McLaren, Donald G.; Ward, Andrew M.; Wigman, Sarah; Sperling, Reisa A.

2014-01-01

Functional connectivity magnetic resonance imaging (fcMRI) is a powerful tool for understanding the network level organization of the brain in research settings and is increasingly being used to study large-scale neuronal network degeneration in clinical trial settings. Presently, a variety of techniques, including seed-based correlation analysis and group independent components analysis (with either dual regression or back projection) are commonly employed to compute functional connectivity metrics. In the present report, we introduce template based rotation,1 a novel analytic approach optimized for use with a priori network parcellations, which may be particularly useful in clinical trial settings. Template based rotation was designed to leverage the stable spatial patterns of intrinsic connectivity derived from out-of-sample datasets by mapping data from novel sessions onto the previously defined a priori templates. We first demonstrate the feasibility of using previously defined a priori templates in connectivity analyses, and then compare the performance of template based rotation to seed based and dual regression methods by applying these analytic approaches to an fMRI dataset of normal young and elderly subjects. We observed that template based rotation and dual regression are approximately equivalent in detecting fcMRI differences between young and old subjects, demonstrating similar effect sizes for group differences and similar reliability metrics across 12 cortical networks. Both template based rotation and dual-regression demonstrated larger effect sizes and comparable reliabilities as compared to seed based correlation analysis, though all three methods yielded similar patterns of network differences. When performing inter-network and sub-network connectivity analyses, we observed that template based rotation offered greater flexibility, larger group differences, and more stable connectivity estimates as compared to dual regression and seed based analyses. This flexibility owes to the reduced spatial and temporal orthogonality constraints of template based rotation as compared to dual regression. These results suggest that template based rotation can provide a useful alternative to existing fcMRI analytic methods, particularly in clinical trial settings where predefined outcome measures and conserved network descriptions across groups are at a premium. PMID:25150630

Sliding mode control-based linear functional observers for discrete-time stochastic systems

NASA Astrophysics Data System (ADS)

Singh, Satnesh; Janardhanan, Sivaramakrishnan

2017-11-01

Sliding mode control (SMC) is one of the most popular techniques to stabilise linear discrete-time stochastic systems. However, application of SMC becomes difficult when the system states are not available for feedback. This paper presents a new approach to design a SMC-based functional observer for discrete-time stochastic systems. The functional observer is based on the Kronecker product approach. Existence conditions and stability analysis of the proposed observer are given. The control input is estimated by a novel linear functional observer. This approach leads to a non-switching type of control, thereby eliminating the fundamental cause of chatter. Furthermore, the functional observer is designed in such a way that the effect of process and measurement noise is minimised. Simulation example is given to illustrate and validate the proposed design method.

On the Implementation and Performance of Iterative Methods for Computational Electromagnetics

DTIC Science & Technology

1985-12-01

based on F3 only if L is self-adjoint and positive definite in the sense of Griffel [651. Each of the above functionals gives rise to a different...New York: Wiley, 1960. [65] D. H. Griffel , Applied Functional Analysis. New York: Wiley, 1981. [661 R. Fletcher and C. M. Reeves, "Functional

Developments in Measuring Functional Activities: Where Do We Go with the PEDI-CAT?

ERIC Educational Resources Information Center

Ketelaar, Marjolijn; Wassenberg-Severijnen, Jeltje

2010-01-01

During the past 30 years many pediatric assessment and outcome measures have been developed. Based on Rasch analysis, the Pediatric Evaluation of Disability Inventory (PEDI) was designed to measure functional status by asking parents about both the skills of their children and the performance of daily tasks in three functionally important domains…

Extracting Intrinsic Functional Networks with Feature-Based Group Independent Component Analysis

ERIC Educational Resources Information Center

Calhoun, Vince D.; Allen, Elena

2013-01-01

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in…

'Whose atlas I use, his song I sing?' - The impact of anatomical atlases on fiber tract contributions to cognitive deficits after stroke.

PubMed

de Haan, Bianca; Karnath, Hans-Otto

2017-12-01

Nowadays, different anatomical atlases exist for the anatomical interpretation of the results from neuroimaging and lesion analysis studies that investigate the contribution of white matter fiber tract integrity to cognitive (dys)function. A major problem with the use of different atlases in different studies, however, is that the anatomical interpretation of neuroimaging and lesion analysis results might vary as a function of the atlas used. This issue might be particularly prominent in studies that investigate the contribution of white matter fiber tract integrity to cognitive (dys)function. We used a single large-sample dataset of right brain damaged stroke patients with and without cognitive deficit (here: spatial neglect) to systematically compare the influence of three different, widely-used white matter fiber tract atlases (1 histology-based atlas and 2 DTI tractography-based atlases) on conclusions concerning the involvement of white matter fiber tracts in the pathogenesis of cognitive dysfunction. We both calculated the overlap between the statistical lesion analysis results and each long association fiber tract (topological analyses) and performed logistic regressions on the extent of fiber tract damage in each individual for each long association white matter fiber tract (hodological analyses). For the topological analyses, our results suggest that studies that use tractography-based atlases are more likely to conclude that white matter integrity is critical for a cognitive (dys)function than studies that use a histology-based atlas. The DTI tractography-based atlases classified approximately 10 times as many voxels of the statistical map as being located in a long association white matter fiber tract than the histology-based atlas. For hodological analyses on the other hand, we observed that the conclusions concerning the overall importance of long association fiber tract integrity to cognitive function do not necessarily depend on the white matter atlas used, but conclusions may vary as a function of atlas used at the level of individual fiber tracts. Moreover, these analyses revealed that hodological studies that express the individual extent of injury to each fiber tract as a binomial variable are more likely to conclude that white matter integrity is critical for a cognitive function than studies that express the individual extent of injury to each fiber tract as a continuous variable. Copyright © 2017 Elsevier Inc. All rights reserved.

Construction and Analysis of Functional Networks in the Gut Microbiome of Type 2 Diabetes Patients.

PubMed

Li, Lianshuo; Wang, Zicheng; He, Peng; Ma, Shining; Du, Jie; Jiang, Rui

2016-10-01

Although networks of microbial species have been widely used in the analysis of 16S rRNA sequencing data of a microbiome, the construction and analysis of a complete microbial gene network are in general problematic because of the large number of microbial genes in metagenomics studies. To overcome this limitation, we propose to map microbial genes to functional units, including KEGG orthologous groups and the evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG) orthologous groups, to enable the construction and analysis of a microbial functional network. We devised two statistical methods to infer pairwise relationships between microbial functional units based on a deep sequencing dataset of gut microbiome from type 2 diabetes (T2D) patients as well as healthy controls. Networks containing such functional units and their significant interactions were constructed subsequently. We conducted a variety of analyses of global properties, local properties, and functional modules in the resulting functional networks. Our data indicate that besides the observations consistent with the current knowledge, this study provides novel biological insights into the gut microbiome associated with T2D. Copyright © 2016. Production and hosting by Elsevier Ltd.

Process-based network decomposition reveals backbone motif structure

PubMed Central

Wang, Guanyu; Du, Chenghang; Chen, Hao; Simha, Rahul; Rong, Yongwu; Xiao, Yi; Zeng, Chen

2010-01-01

A central challenge in systems biology today is to understand the network of interactions among biomolecules and, especially, the organizing principles underlying such networks. Recent analysis of known networks has identified small motifs that occur ubiquitously, suggesting that larger networks might be constructed in the manner of electronic circuits by assembling groups of these smaller modules. Using a unique process-based approach to analyzing such networks, we show for two cell-cycle networks that each of these networks contains a giant backbone motif spanning all the network nodes that provides the main functional response. The backbone is in fact the smallest network capable of providing the desired functionality. Furthermore, the remaining edges in the network form smaller motifs whose role is to confer stability properties rather than provide function. The process-based approach used in the above analysis has additional benefits: It is scalable, analytic (resulting in a single analyzable expression that describes the behavior), and computationally efficient (all possible minimal networks for a biological process can be identified and enumerated). PMID:20498084

Simulation analysis of a microcomputer-based, low-cost Omega navigation system

NASA Technical Reports Server (NTRS)

Lilley, R. W.; Salter, R. J., Jr.

1976-01-01

The current status of research on a proposed micro-computer-based, low-cost Omega Navigation System (ONS) is described. The design approach emphasizes minimum hardware, maximum software, and the use of a low-cost, commercially-available microcomputer. Currently under investigation is the implementation of a low-cost navigation processor and its interface with an omega sensor to complete the hardware-based ONS. Sensor processor functions are simulated to determine how many of the sensor processor functions can be handled by innovative software. An input data base of live Omega ground and flight test data was created. The Omega sensor and microcomputer interface modules used to collect the data are functionally described. Automatic synchronization to the Omega transmission pattern is described as an example of the algorithms developed using this data base.

VESUVIO Data Analysis Goes MANTID

NASA Astrophysics Data System (ADS)

Jackson, S.; Krzystyniak, M.; Seel, A. G.; Gigg, M.; Richards, S. E.; Fernandez-Alonso, F.

2014-12-01

This paper describes ongoing efforts to implement the reduction and analysis of neutron Compton scattering data within the MANTID framework. Recently, extensive work has been carried out to integrate the bespoke data reduction and analysis routines written for VESUVIO with the MANTID framework. While the programs described in this document are designed to replicate the functionality of the Fortran and Genie routines already in use, most of them have been written from scratch and are not based on the original code base.

[Formula: see text] regularity properties of singular parameterizations in isogeometric analysis.

PubMed

Takacs, T; Jüttler, B

2012-11-01

Isogeometric analysis (IGA) is a numerical simulation method which is directly based on the NURBS-based representation of CAD models. It exploits the tensor-product structure of 2- or 3-dimensional NURBS objects to parameterize the physical domain. Hence the physical domain is parameterized with respect to a rectangle or to a cube. Consequently, singularly parameterized NURBS surfaces and NURBS volumes are needed in order to represent non-quadrangular or non-hexahedral domains without splitting, thereby producing a very compact and convenient representation. The Galerkin projection introduces finite-dimensional spaces of test functions in the weak formulation of partial differential equations. In particular, the test functions used in isogeometric analysis are obtained by composing the inverse of the domain parameterization with the NURBS basis functions. In the case of singular parameterizations, however, some of the resulting test functions do not necessarily fulfill the required regularity properties. Consequently, numerical methods for the solution of partial differential equations cannot be applied properly. We discuss the regularity properties of the test functions. For one- and two-dimensional domains we consider several important classes of singularities of NURBS parameterizations. For specific cases we derive additional conditions which guarantee the regularity of the test functions. In addition we present a modification scheme for the discretized function space in case of insufficient regularity. It is also shown how these results can be applied for computational domains in higher dimensions that can be parameterized via sweeping.

Functional brain networks in Alzheimer's disease: EEG analysis based on limited penetrable visibility graph and phase space method

NASA Astrophysics Data System (ADS)

Wang, Jiang; Yang, Chen; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

2016-10-01

In this paper, EEG series are applied to construct functional connections with the correlation between different regions in order to investigate the nonlinear characteristic and the cognitive function of the brain with Alzheimer's disease (AD). First, limited penetrable visibility graph (LPVG) and phase space method map single EEG series into networks, and investigate the underlying chaotic system dynamics of AD brain. Topological properties of the networks are extracted, such as average path length and clustering coefficient. It is found that the network topology of AD in several local brain regions are different from that of the control group with no statistically significant difference existing all over the brain. Furthermore, in order to detect the abnormality of AD brain as a whole, functional connections among different brain regions are reconstructed based on similarity of clustering coefficient sequence (CCSS) of EEG series in the four frequency bands (delta, theta, alpha, and beta), which exhibit obvious small-world properties. Graph analysis demonstrates that for both methodologies, the functional connections between regions of AD brain decrease, particularly in the alpha frequency band. AD causes the graph index complexity of the functional network decreased, the small-world properties weakened, and the vulnerability increased. The obtained results show that the brain functional network constructed by LPVG and phase space method might be more effective to distinguish AD from the normal control than the analysis of single series, which is helpful for revealing the underlying pathological mechanism of the disease.

The effect of cognitive-based training for the healthy older people: A meta-analysis of randomized controlled trials

PubMed Central

Chiu, Huei-Ling; Chu, Hsin; Tsai, Jui-Chen; Liu, Doresses; Chen, Ying-Ren; Yang, Hui-Ling

2017-01-01

Background From the perspective of disease prevention, the enhancement of cognitive function among the healthy older people has become an important issue in many countries lately. This study aim to investigate the effect of cognitive-based training on the overall cognitive function, memory, attention, executive function, and visual-spatial ability of the healthy older people. Methods Cochrane, PubMed, EMBASE, MEDLINE, PsycINFO, and CINAHL of selected randomized controlled trials (RCTs), and previous systematic reviews were searched for eligible studies. The population focused on this study were healthy older people who participated in randomized controlled trials that investigated the effectiveness of cognitive-based training. The outcomes including change in overall cognitive function, memory, attention, executive function, and visual-spatial ability. Results We collected a total of 31 RCTs, the results showed that cognitive-based training has a moderate effect on overall cognitive function (g = 0.419; 95%CI = 0.205–0.634) and executive function (g = 0.420; 95%CI = 0.239–0.602), and a small effect on the memory (g = 0.354; 95%CI = 0.244–0.465), attention (g = 0.218; 95%CI = 0.125–0.311), and visual-spatial ability (g = 0.183;95%CI = 0.015–0.352) in healthy older people. Subgroup analysis indicated the intervention characteristics of ≧3 times each week (p = 0.042), ≧8 total training weeks (p = 0.003) and ≧24 total training sessions (p = 0.040) yields a greater effect size. Conclusions Cognitive-based training is effective for the healthy older people. This improvement can represent a clinically important benefit, provide information about the use of cognitive-based training in healthy older people, and help the healthy older people obtain the greatest possible benefit in health promotion and disease prevention. PMID:28459873

GeneSCF: a real-time based functional enrichment tool with support for multiple organisms.

PubMed

Subhash, Santhilal; Kanduri, Chandrasekhar

2016-09-13

High-throughput technologies such as ChIP-sequencing, RNA-sequencing, DNA sequencing and quantitative metabolomics generate a huge volume of data. Researchers often rely on functional enrichment tools to interpret the biological significance of the affected genes from these high-throughput studies. However, currently available functional enrichment tools need to be updated frequently to adapt to new entries from the functional database repositories. Hence there is a need for a simplified tool that can perform functional enrichment analysis by using updated information directly from the source databases such as KEGG, Reactome or Gene Ontology etc. In this study, we focused on designing a command-line tool called GeneSCF (Gene Set Clustering based on Functional annotations), that can predict the functionally relevant biological information for a set of genes in a real-time updated manner. It is designed to handle information from more than 4000 organisms from freely available prominent functional databases like KEGG, Reactome and Gene Ontology. We successfully employed our tool on two of published datasets to predict the biologically relevant functional information. The core features of this tool were tested on Linux machines without the need for installation of more dependencies. GeneSCF is more reliable compared to other enrichment tools because of its ability to use reference functional databases in real-time to perform enrichment analysis. It is an easy-to-integrate tool with other pipelines available for downstream analysis of high-throughput data. More importantly, GeneSCF can run multiple gene lists simultaneously on different organisms thereby saving time for the users. Since the tool is designed to be ready-to-use, there is no need for any complex compilation and installation procedures.

On the Power of Abstract Interpretation

NASA Technical Reports Server (NTRS)

Reddy, Uday S.; Kamin, Samuel N.

1991-01-01

Increasingly sophisticated applications of static analysis place increased burden on the reliability of the analysis techniques. Often, the failure of the analysis technique to detect some information my mean that the time or space complexity of the generated code would be altered. Thus, it is important to precisely characterize the power of static analysis techniques. We follow the approach of Selur et. al. who studied the power of strictness analysis techniques. Their result can be summarized by saying 'strictness analysis is perfect up to variations in constants.' In other words, strictness analysis is as good as it could be, short of actually distinguishing between concrete values. We use this approach to characterize a broad class of analysis techniques based on abstract interpretation including, but not limited to, strictness analysis. For the first-order case, we consider abstract interpretations where the abstract domain for data values is totally ordered. This condition is satisfied by Mycroft's strictness analysis that of Sekar et. al. and Wadler's analysis of list-strictness. For such abstract interpretations, we show that the analysis is complete in the sense that, short of actually distinguishing between concrete values with the same abstraction, it gives the best possible information. We further generalize these results to typed lambda calculus with pairs and higher-order functions. Note that products and function spaces over totally ordered domains are not totally ordered. In fact, the notion of completeness used in the first-order case fails if product domains or function spaces are added. We formulate a weaker notion of completeness based on observability of values. Two values (including pairs and functions) are considered indistinguishable if their observable components are indistinguishable. We show that abstract interpretation of typed lambda calculus programs is complete up to this notion of indistinguishability. We use denotationally-oriented arguments instead of the detailed operational arguments used by Selur et. al.. Hence, our proofs are much simpler. They should be useful for further future improvements.

ABERRANT RESTING-STATE BRAIN ACTIVITY IN POSTTRAUMATIC STRESS DISORDER: A META-ANALYSIS AND SYSTEMATIC REVIEW.

PubMed

Koch, Saskia B J; van Zuiden, Mirjam; Nawijn, Laura; Frijling, Jessie L; Veltman, Dick J; Olff, Miranda

2016-07-01

About 10% of trauma-exposed individuals develop PTSD. Although a growing number of studies have investigated resting-state abnormalities in PTSD, inconsistent results suggest a need for a meta-analysis and a systematic review. We conducted a systematic literature search in four online databases using keywords for PTSD, functional neuroimaging, and resting-state. In total, 23 studies matched our eligibility criteria. For the meta-analysis, we included 14 whole-brain resting-state studies, reporting data on 663 participants (298 PTSD patients and 365 controls). We used the activation likelihood estimation approach to identify concurrence of whole-brain hypo- and hyperactivations in PTSD patients during rest. Seed-based studies could not be included in the quantitative meta-analysis. Therefore, a separate qualitative systematic review was conducted on nine seed-based functional connectivity studies. The meta-analysis showed consistent hyperactivity in the ventral anterior cingulate cortex and the parahippocampus/amygdala, but hypoactivity in the (posterior) insula, cerebellar pyramis and middle frontal gyrus in PTSD patients, compared to healthy controls. Partly concordant with these findings, the systematic review on seed-based functional connectivity studies showed enhanced salience network (SN) connectivity, but decreased default mode network (DMN) connectivity in PTSD. Combined, these altered resting-state connectivity and activity patterns could represent neurobiological correlates of increased salience processing and hypervigilance (SN), at the cost of awareness of internal thoughts and autobiographical memory (DMN) in PTSD. However, several discrepancies between findings of the meta-analysis and systematic review were observed, stressing the need for future studies on resting-state abnormalities in PTSD patients. © 2016 Wiley Periodicals, Inc.

[Standardization of the terms for Chinese herbal functions based on functional targeting].

PubMed

Xiao, Bin; Tao, Ou; Gu, Hao; Wang, Yun; Qiao, Yan-Jiang

2011-03-01

Functional analysis concisely summarizes and concentrates on the therapeutic characteristics and features of Chinese herbal medicine. Standardization of the terms for Chinese herbal functions not only plays a key role in modern research and development of Chinese herbal medicine, but also has far-reaching clinical applications. In this paper, a new method for standardizing the terms for Chinese herbal function was proposed. Firstly, functional targets were collected. Secondly, the pathological conditions and the mode of action of every functional target were determined by analyzing the references. Thirdly, the relationships between the pathological condition and the mode of action were determined based on Chinese medicine theory and data. This three-step approach allows for standardization of the terms for Chinese herbal functions. Promoting the standardization of Chinese medicine terms will benefit the overall clinical application of Chinese herbal medicine.

Decomposition of Proteins into Dynamic Units from Atomic Cross-Correlation Functions.

PubMed

Calligari, Paolo; Gerolin, Marco; Abergel, Daniel; Polimeno, Antonino

2017-01-10

In this article, we present a clustering method of atoms in proteins based on the analysis of the correlation times of interatomic distance correlation functions computed from MD simulations. The goal is to provide a coarse-grained description of the protein in terms of fewer elements that can be treated as dynamically independent subunits. Importantly, this domain decomposition method does not take into account structural properties of the protein. Instead, the clustering of protein residues in terms of networks of dynamically correlated domains is defined on the basis of the effective correlation times of the pair distance correlation functions. For these properties, our method stands as a complementary analysis to the customary protein decomposition in terms of quasi-rigid, structure-based domains. Results obtained for a prototypal protein structure illustrate the approach proposed.

Systematic Characterization and Analysis of the Taxonomic Drivers of Functional Shifts in the Human Microbiome.

PubMed

Manor, Ohad; Borenstein, Elhanan

2017-02-08

Comparative analyses of the human microbiome have identified both taxonomic and functional shifts that are associated with numerous diseases. To date, however, microbiome taxonomy and function have mostly been studied independently and the taxonomic drivers of functional imbalances have not been systematically identified. Here, we present FishTaco, an analytical and computational framework that integrates taxonomic and functional comparative analyses to accurately quantify taxon-level contributions to disease-associated functional shifts. Applying FishTaco to several large-scale metagenomic cohorts, we show that shifts in the microbiome's functional capacity can be traced back to specific taxa. Furthermore, the set of taxa driving functional shifts and their contribution levels vary markedly between functions. We additionally find that similar functional imbalances in different diseases are driven by both disease-specific and shared taxa. Such integrated analysis of microbiome ecological and functional dynamics can inform future microbiome-based therapy, pinpointing putative intervention targets for manipulating the microbiome's functional capacity. Copyright © 2017 Elsevier Inc. All rights reserved.

Analysis and selection of optimal function implementations in massively parallel computer

DOEpatents

Archer, Charles Jens [Rochester, MN; Peters, Amanda [Rochester, MN; Ratterman, Joseph D [Rochester, MN

2011-05-31

An apparatus, program product and method optimize the operation of a parallel computer system by, in part, collecting performance data for a set of implementations of a function capable of being executed on the parallel computer system based upon the execution of the set of implementations under varying input parameters in a plurality of input dimensions. The collected performance data may be used to generate selection program code that is configured to call selected implementations of the function in response to a call to the function under varying input parameters. The collected performance data may be used to perform more detailed analysis to ascertain the comparative performance of the set of implementations of the function under the varying input parameters.

An Analysis of Risk and Function Information in Early Stage Design

NASA Technical Reports Server (NTRS)

Barrientos, Francesca; Tumer, Irem; Grantham, Katie; VanWie, Michael; Stone, Robert

2005-01-01

The concept of function offers a high potential for thinking and reasoning about designs as well as providing a common thread for relating together other design information. This paper focuses specifically on the relation between function and risk by examining how this information is addressed for a design team conducting early stage design for space missions. Risk information is decomposed into a set of key attributes which are then used to scrutinize the risk information using three approaches from the pragmatics sub-field of linguistics: i) Gricean, ii) Relevance Theory, and Functional Analysis. Results of this linguistics-based approach descriptively account for the context of designer communication with respect to function and risk, and offer prescriptive guidelines for improving designer communication.

Identifying Dynamic Functional Connectivity Changes in Dementia with Lewy Bodies Based on Product Hidden Markov Models.

PubMed

Sourty, Marion; Thoraval, Laurent; Roquet, Daniel; Armspach, Jean-Paul; Foucher, Jack; Blanc, Frédéric

2016-01-01

Exploring time-varying connectivity networks in neurodegenerative disorders is a recent field of research in functional MRI. Dementia with Lewy bodies (DLB) represents 20% of the neurodegenerative forms of dementia. Fluctuations of cognition and vigilance are the key symptoms of DLB. To date, no dynamic functional connectivity (DFC) investigations of this disorder have been performed. In this paper, we refer to the concept of connectivity state as a piecewise stationary configuration of functional connectivity between brain networks. From this concept, we propose a new method for group-level as well as for subject-level studies to compare and characterize connectivity state changes between a set of resting-state networks (RSNs). Dynamic Bayesian networks, statistical and graph theory-based models, enable one to learn dependencies between interacting state-based processes. Product hidden Markov models (PHMM), an instance of dynamic Bayesian networks, are introduced here to capture both statistical and temporal aspects of DFC of a set of RSNs. This analysis was based on sliding-window cross-correlations between seven RSNs extracted from a group independent component analysis performed on 20 healthy elderly subjects and 16 patients with DLB. Statistical models of DFC differed in patients compared to healthy subjects for the occipito-parieto-frontal network, the medial occipital network and the right fronto-parietal network. In addition, pairwise comparisons of DFC of RSNs revealed a decrease of dependency between these two visual networks (occipito-parieto-frontal and medial occipital networks) and the right fronto-parietal control network. The analysis of DFC state changes thus pointed out networks related to the cognitive functions that are known to be impaired in DLB: visual processing as well as attentional and executive functions. Besides this context, product HMM applied to RSNs cross-correlations offers a promising new approach to investigate structural and temporal aspects of brain DFC.

MiRGOFS: A GO-based functional similarity measure for miRNAs, with applications to the prediction of miRNA subcellular localization and miRNA-disease association.

PubMed

Yang, Yang; Fu, Xiaofeng; Qu, Wenhao; Xiao, Yiqun; Shen, Hong-Bin

2018-04-27

Benefiting from high-throughput experimental technologies, whole-genome analysis of microRNAs (miRNAs) has been more and more common to uncover important regulatory roles of miRNAs and identify miRNA biomarkers for disease diagnosis. As a complementary information to the high-throughput experimental data, domain knowledge like the Gene Ontology and KEGG pathway is usually used to guide gene function analysis. However, functional annotation for miRNAs is scarce in the public databases. Till now, only a few methods have been proposed for measuring the functional similarity between miRNAs based on public annotation data, and these methods cover a very limited number of miRNAs, which are not applicable to large-scale miRNA analysis. In this paper, we propose a new method to measure the functional similarity for miRNAs, called miRGOFS, which has two notable features: I) it adopts a new GO semantic similarity metric which considers both common ancestors and descendants of GO terms; II) it computes similarity between GO sets in an asymmetric manner, and weights each GO term by its statistical significance. The miRGOFS-based predictor achieves an F1 of 61.2% on a benchmark data set of miRNA localization, and AUC values of 87.7% and 81.1% on two benchmark sets of miRNA-disease association, respectively. Compared with the existing functional similarity measurements of miRNAs, miRGOFS has the advantages of higher accuracy and larger coverage of human miRNAs (over 1000 miRNAs). http://www.csbio.sjtu.edu.cn/bioinf/MiRGOFS/. yangyang@cs.sjtu.edu.cn or hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online.

Software Construction and Analysis Tools for Future Space Missions

NASA Technical Reports Server (NTRS)

Lowry, Michael R.; Clancy, Daniel (Technical Monitor)

2002-01-01

NASA and its international partners will increasingly depend on software-based systems to implement advanced functions for future space missions, such as Martian rovers that autonomously navigate long distances exploring geographic features formed by surface water early in the planet's history. The software-based functions for these missions will need to be robust and highly reliable, raising significant challenges in the context of recent Mars mission failures attributed to software faults. After reviewing these challenges, this paper describes tools that have been developed at NASA Ames that could contribute to meeting these challenges; 1) Program synthesis tools based on automated inference that generate documentation for manual review and annotations for automated certification. 2) Model-checking tools for concurrent object-oriented software that achieve memorability through synergy with program abstraction and static analysis tools.

The Interaction Properties of the Human Rab GTPase Family – A Comparative Analysis Reveals Determinants of Molecular Binding Selectivity

PubMed Central

Stein, Matthias; Pilli, Manohar; Bernauer, Sabine; Habermann, Bianca H.; Zerial, Marino; Wade, Rebecca C.

2012-01-01

Background Rab GTPases constitute the largest subfamily of the Ras protein superfamily. Rab proteins regulate organelle biogenesis and transport, and display distinct binding preferences for effector and activator proteins, many of which have not been elucidated yet. The underlying molecular recognition motifs, binding partner preferences and selectivities are not well understood. Methodology/Principal Findings Comparative analysis of the amino acid sequences and the three-dimensional electrostatic and hydrophobic molecular interaction fields of 62 human Rab proteins revealed a wide range of binding properties with large differences between some Rab proteins. This analysis assists the functional annotation of Rab proteins 12, 14, 26, 37 and 41 and provided an explanation for the shared function of Rab3 and 27. Rab7a and 7b have very different electrostatic potentials, indicating that they may bind to different effector proteins and thus, exert different functions. The subfamily V Rab GTPases which are associated with endosome differ subtly in the interaction properties of their switch regions, and this may explain exchange factor specificity and exchange kinetics. Conclusions/Significance We have analysed conservation of sequence and of molecular interaction fields to cluster and annotate the human Rab proteins. The analysis of three dimensional molecular interaction fields provides detailed insight that is not available from a sequence-based approach alone. Based on our results, we predict novel functions for some Rab proteins and provide insights into their divergent functions and the determinants of their binding partner selectivity. PMID:22523562

Visual target modulation of functional connectivity networks revealed by self-organizing group ICA.

PubMed

van de Ven, Vincent; Bledowski, Christoph; Prvulovic, David; Goebel, Rainer; Formisano, Elia; Di Salle, Francesco; Linden, David E J; Esposito, Fabrizio

2008-12-01

We applied a data-driven analysis based on self-organizing group independent component analysis (sogICA) to fMRI data from a three-stimulus visual oddball task. SogICA is particularly suited to the investigation of the underlying functional connectivity and does not rely on a predefined model of the experiment, which overcomes some of the limitations of hypothesis-driven analysis. Unlike most previous applications of ICA in functional imaging, our approach allows the analysis of the data at the group level, which is of particular interest in high order cognitive studies. SogICA is based on the hierarchical clustering of spatially similar independent components, derived from single subject decompositions. We identified four main clusters of components, centered on the posterior cingulate, bilateral insula, bilateral prefrontal cortex, and right posterior parietal and prefrontal cortex, consistently across all participants. Post hoc comparison of time courses revealed that insula, prefrontal cortex and right fronto-parietal components showed higher activity for targets than for distractors. Activation for distractors was higher in the posterior cingulate cortex, where deactivation was observed for targets. While our results conform to previous neuroimaging studies, they also complement conventional results by showing functional connectivity networks with unique contributions to the task that were consistent across subjects. SogICA can thus be used to probe functional networks of active cognitive tasks at the group-level and can provide additional insights to generate new hypotheses for further study. Copyright 2007 Wiley-Liss, Inc.

Characterizing Individual Differences in Functional Connectivity Using Dual-Regression and Seed-Based Approaches

PubMed Central

Smith, David V.; Utevsky, Amanda V.; Bland, Amy R.; Clement, Nathan; Clithero, John A.; Harsch, Anne E. W.; Carter, R. McKell; Huettel, Scott A.

2014-01-01

A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent components analysis (ICA). We estimated voxelwise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal-parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust—yet frequently ignored—neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity. PMID:24662574

Structural and functional brain changes in early- and mid-stage primary open-angle glaucoma using voxel-based morphometry and functional magnetic resonance imaging.

PubMed

Jiang, Ming-Ming; Zhou, Qing; Liu, Xiao-Yong; Shi, Chang-Zheng; Chen, Jian; Huang, Xiang-He

2017-03-01

To investigate structural and functional brain changes in patients with primary open-angle glaucoma (POAG) by using voxel-based morphometry based on diffeomorphic anatomical registration through exponentiated Lie algebra (VBM-DARTEL) and blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI), respectively.Thirteen patients diagnosed with POAG and 13 age- and sex-matched healthy controls were enrolled in the study. For each participant, high-resolution structural brain imaging and blood flow imaging were acquired on a 3.0-Tesla magnetic resonance imaging (MRI) scanner. Structural and functional changes between the POAG and control groups were analyzed. An analysis was carried out to identify correlations between structural and functional changes acquired in the previous analysis and the retinal nerve fiber layer (RNFL).Patients in the POAG group showed a significant (P < 0.001) volume increase in the midbrain, left brainstem, frontal gyrus, cerebellar vermis, left inferior parietal lobule, caudate nucleus, thalamus, precuneus, and Brodmann areas 7, 18, and 46. Moreover, significant (P < 0.001) BOLD signal changes were observed in the right supramarginal gyrus, frontal gyrus, superior frontal gyrus, left inferior parietal lobule, left cuneus, and left midcingulate area; many of these regions had high correlations with the RNFL.Patients with POAG undergo widespread and complex changes in cortical brain structure and blood flow. (ClinicalTrials.gov number: NCT02570867).

An NCME Instructional Module on Latent DIF Analysis Using Mixture Item Response Models

ERIC Educational Resources Information Center

Cho, Sun-Joo; Suh, Youngsuk; Lee, Woo-yeol

2016-01-01

The purpose of this ITEMS module is to provide an introduction to differential item functioning (DIF) analysis using mixture item response models. The mixture item response models for DIF analysis involve comparing item profiles across latent groups, instead of manifest groups. First, an overview of DIF analysis based on latent groups, called…

Rank Determination of Mental Functions by 1D Wavelets and Partial Correlation.

PubMed

Karaca, Y; Aslan, Z; Cattani, C; Galletta, D; Zhang, Y

2017-01-01

The main aim of this paper is to classify mental functions by the Wechsler Adult Intelligence Scale-Revised tests with a mixed method based on wavelets and partial correlation. The Wechsler Adult Intelligence Scale-Revised is a widely used test designed and applied for the classification of the adults cognitive skills in a comprehensive manner. In this paper, many different intellectual profiles have been taken into consideration to measure the relationship between the mental functioning and psychological disorder. We propose a method based on wavelets and correlation analysis for classifying mental functioning, by the analysis of some selected parameters measured by the Wechsler Adult Intelligence Scale-Revised tests. In particular, 1-D Continuous Wavelet Analysis, 1-D Wavelet Coefficient Method and Partial Correlation Method have been analyzed on some Wechsler Adult Intelligence Scale-Revised parameters such as School Education, Gender, Age, Performance Information Verbal and Full Scale Intelligence Quotient. In particular, we will show that gender variable has a negative but a significant role on age and Performance Information Verbal factors. The age parameters also has a significant relation in its role on Performance Information Verbal and Full Scale Intelligence Quotient change.

Diversity and functions of bacterial community in drinking water biofilms revealed by high-throughput sequencing

PubMed Central

Chao, Yuanqing; Mao, Yanping; Wang, Zhiping; Zhang, Tong

2015-01-01

The development of biofilms in drinking water (DW) systems may cause various problems to water quality. To investigate the community structure of biofilms on different pipe materials and the global/specific metabolic functions of DW biofilms, PCR-based 454 pyrosequencing data for 16S rRNA genes and Illumina metagenomic data were generated and analysed. Considerable differences in bacterial diversity and taxonomic structure were identified between biofilms formed on stainless steel and biofilms formed on plastics, indicating that the metallic materials facilitate the formation of higher diversity biofilms. Moreover, variations in several dominant genera were observed during biofilm formation. Based on PCA analysis, the global functions in the DW biofilms were similar to other DW metagenomes. Beyond the global functions, the occurrences and abundances of specific protective genes involved in the glutathione metabolism, the SoxRS system, the OxyR system, RpoS regulated genes, and the production/degradation of extracellular polymeric substances were also evaluated. A near-complete and low-contamination draft genome was constructed from the metagenome of the DW biofilm, based on the coverage and tetranucleotide frequencies, and identified as a Bradyrhizobiaceae-like bacterium according to a phylogenetic analysis. Our findings provide new insight into DW biofilms, especially in terms of their metabolic functions. PMID:26067561

Diversity and functions of bacterial community in drinking water biofilms revealed by high-throughput sequencing

NASA Astrophysics Data System (ADS)

Chao, Yuanqing; Mao, Yanping; Wang, Zhiping; Zhang, Tong

2015-06-01

The development of biofilms in drinking water (DW) systems may cause various problems to water quality. To investigate the community structure of biofilms on different pipe materials and the global/specific metabolic functions of DW biofilms, PCR-based 454 pyrosequencing data for 16S rRNA genes and Illumina metagenomic data were generated and analysed. Considerable differences in bacterial diversity and taxonomic structure were identified between biofilms formed on stainless steel and biofilms formed on plastics, indicating that the metallic materials facilitate the formation of higher diversity biofilms. Moreover, variations in several dominant genera were observed during biofilm formation. Based on PCA analysis, the global functions in the DW biofilms were similar to other DW metagenomes. Beyond the global functions, the occurrences and abundances of specific protective genes involved in the glutathione metabolism, the SoxRS system, the OxyR system, RpoS regulated genes, and the production/degradation of extracellular polymeric substances were also evaluated. A near-complete and low-contamination draft genome was constructed from the metagenome of the DW biofilm, based on the coverage and tetranucleotide frequencies, and identified as a Bradyrhizobiaceae-like bacterium according to a phylogenetic analysis. Our findings provide new insight into DW biofilms, especially in terms of their metabolic functions.

Diversity and functions of bacterial community in drinking water biofilms revealed by high-throughput sequencing.

PubMed

Chao, Yuanqing; Mao, Yanping; Wang, Zhiping; Zhang, Tong

2015-06-12

The development of biofilms in drinking water (DW) systems may cause various problems to water quality. To investigate the community structure of biofilms on different pipe materials and the global/specific metabolic functions of DW biofilms, PCR-based 454 pyrosequencing data for 16S rRNA genes and Illumina metagenomic data were generated and analysed. Considerable differences in bacterial diversity and taxonomic structure were identified between biofilms formed on stainless steel and biofilms formed on plastics, indicating that the metallic materials facilitate the formation of higher diversity biofilms. Moreover, variations in several dominant genera were observed during biofilm formation. Based on PCA analysis, the global functions in the DW biofilms were similar to other DW metagenomes. Beyond the global functions, the occurrences and abundances of specific protective genes involved in the glutathione metabolism, the SoxRS system, the OxyR system, RpoS regulated genes, and the production/degradation of extracellular polymeric substances were also evaluated. A near-complete and low-contamination draft genome was constructed from the metagenome of the DW biofilm, based on the coverage and tetranucleotide frequencies, and identified as a Bradyrhizobiaceae-like bacterium according to a phylogenetic analysis. Our findings provide new insight into DW biofilms, especially in terms of their metabolic functions.

Giovanni: The Bridge between Data and Science

NASA Technical Reports Server (NTRS)

Shen, Suhung; Lynnes, Christopher; Kempler, Steven J.

2012-01-01

NASA Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) is a web-based remote sensing and model data visualization and analysis system developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). This web-based tool facilitates data discovery, exploration and analysis of large amount of global and regional data sets, covering atmospheric dynamics, atmospheric chemistry, hydrology, oceanographic, and land surface. Data analysis functions include Lat-Lon map, time series, scatter plot, correlation map, difference, cross-section, vertical profile, and animation etc. Visualization options enable comparisons of multiple variables and easier refinement. Recently, new features have been developed, such as interactive scatter plots and maps. The performance is also being improved, in some cases by an order of magnitude for certain analysis functions with optimized software. We are working toward merging current Giovanni portals into a single omnibus portal with all variables in one (virtual) location to help users find a variable easily and enhance the intercomparison capability

Parametric fMRI of paced motor responses uncovers novel whole-brain imaging biomarkers in spinocerebellar ataxia type 3.

PubMed

Duarte, João Valente; Faustino, Ricardo; Lobo, Mercês; Cunha, Gil; Nunes, César; Ferreira, Carlos; Januário, Cristina; Castelo-Branco, Miguel

2016-10-01

Machado-Joseph Disease, inherited type 3 spinocerebellar ataxia (SCA3), is the most common form worldwide. Neuroimaging and neuropathology have consistently demonstrated cerebellar alterations. Here we aimed to discover whole-brain functional biomarkers, based on parametric performance-level-dependent signals. We assessed 13 patients with early SCA3 and 14 healthy participants. We used a combined parametric behavioral/functional neuroimaging design to investigate disease fingerprints, as a function of performance levels, coupled with structural MRI and voxel-based morphometry. Functional magnetic resonance imaging (fMRI) was designed to parametrically analyze behavior and neural responses to audio-paced bilateral thumb movements at temporal frequencies of 1, 3, and 5 Hz. Our performance-level-based design probing neuronal correlates of motor coordination enabled the discovery that neural activation and behavior show critical loss of parametric modulation specifically in SCA3, associated with frequency-dependent cortico/subcortical activation/deactivation patterns. Cerebellar/cortical rate-dependent dissociation patterns could clearly differentiate between groups irrespective of grey matter loss. Our findings suggest functional reorganization of the motor network and indicate a possible role of fMRI as a tool to monitor disease progression in SCA3. Accordingly, fMRI patterns proved to be potential biomarkers in early SCA3, as tested by receiver operating characteristic analysis of both behavior and neural activation at different frequencies. Discrimination analysis based on BOLD signal in response to the applied parametric finger-tapping task significantly often reached >80% sensitivity and specificity in single regions-of-interest.Functional fingerprints based on cerebellar and cortical BOLD performance dependent signal modulation can thus be combined as diagnostic and/or therapeutic targets in hereditary ataxia. Hum Brain Mapp 37:3656-3668, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Wavelet analysis techniques applied to removing varying spectroscopic background in calibration model for pear sugar content

NASA Astrophysics Data System (ADS)

Liu, Yande; Ying, Yibin; Lu, Huishan; Fu, Xiaping

2005-11-01

A new method is proposed to eliminate the varying background and noise simultaneously for multivariate calibration of Fourier transform near infrared (FT-NIR) spectral signals. An ideal spectrum signal prototype was constructed based on the FT-NIR spectrum of fruit sugar content measurement. The performances of wavelet based threshold de-noising approaches via different combinations of wavelet base functions were compared. Three families of wavelet base function (Daubechies, Symlets and Coiflets) were applied to estimate the performance of those wavelet bases and threshold selection rules by a series of experiments. The experimental results show that the best de-noising performance is reached via the combinations of Daubechies 4 or Symlet 4 wavelet base function. Based on the optimization parameter, wavelet regression models for sugar content of pear were also developed and result in a smaller prediction error than a traditional Partial Least Squares Regression (PLSR) mode.

Modeling of 2D diffusion processes based on microscopy data: parameter estimation and practical identifiability analysis.

PubMed

Hock, Sabrina; Hasenauer, Jan; Theis, Fabian J

2013-01-01

Diffusion is a key component of many biological processes such as chemotaxis, developmental differentiation and tissue morphogenesis. Since recently, the spatial gradients caused by diffusion can be assessed in-vitro and in-vivo using microscopy based imaging techniques. The resulting time-series of two dimensional, high-resolutions images in combination with mechanistic models enable the quantitative analysis of the underlying mechanisms. However, such a model-based analysis is still challenging due to measurement noise and sparse observations, which result in uncertainties of the model parameters. We introduce a likelihood function for image-based measurements with log-normal distributed noise. Based upon this likelihood function we formulate the maximum likelihood estimation problem, which is solved using PDE-constrained optimization methods. To assess the uncertainty and practical identifiability of the parameters we introduce profile likelihoods for diffusion processes. As proof of concept, we model certain aspects of the guidance of dendritic cells towards lymphatic vessels, an example for haptotaxis. Using a realistic set of artificial measurement data, we estimate the five kinetic parameters of this model and compute profile likelihoods. Our novel approach for the estimation of model parameters from image data as well as the proposed identifiability analysis approach is widely applicable to diffusion processes. The profile likelihood based method provides more rigorous uncertainty bounds in contrast to local approximation methods.

Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model.

PubMed

Ma, Jing; Yu, Jiong; Hao, Guangshu; Wang, Dan; Sun, Yanni; Lu, Jianxin; Cao, Hongcui; Lin, Feiyan

2017-02-20

The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people. A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL). The back propagation artificial neural network (BP-ANN) was applied to develop prediction models of fs-TG and fs-TC. The results showed there was significant difference in biochemical indexes between healthy people and overweight people. The correlation analysis showed fs-TG was related to weight, height, blood glucose, and indexes of liver and kidney function; while fs-TC was correlated with age, indexes of liver function (P < 0.01). The MRL analysis indicated regression equations of fs-TG and fs-TC both had statistic significant (P < 0.01) when included independent indexes. The BP-ANN model of fs-TG reached training goal at 59 epoch, while fs-TC model achieved high prediction accuracy after training 1000 epoch. In conclusions, there was high relationship of fs-TG and fs-TC with weight, height, age, blood glucose, indexes of liver function and kidney function. Based on related variables, the indexes of fs-TG and fs-TC can be predicted by BP-ANN models in overweight people.

Genetic diversity assessment of anoxygenic photosynthetic bacteria by distance-based grouping analysis of pufM sequences.

PubMed

Zeng, Y H; Chen, X H; Jiao, N Z

2007-12-01

To assess how completely the diversity of anoxygenic phototrophic bacteria (APB) was sampled in natural environments. All nucleotide sequences of the APB marker gene pufM from cultures and environmental clones were retrieved from the GenBank database. A set of cutoff values (sequence distances 0.06, 0.15 and 0.48 for species, genus, and (sub)phylum levels, respectively) was established using a distance-based grouping program. Analysis of the environmental clones revealed that current efforts on APB isolation and sampling in natural environments are largely inadequate. Analysis of the average distance between each identified genus and an uncultured environmental pufM sequence indicated that the majority of cultured APB genera lack environmental representatives. The distance-based grouping method is fast and efficient for bulk functional gene sequences analysis. The results clearly show that we are at a relatively early stage in sampling the global richness of APB species. Periodical assessment will undoubtedly facilitate in-depth analysis of potential biogeographical distribution pattern of APB. This is the first attempt to assess the present understanding of APB diversity in natural environments. The method used is also useful for assessing the diversity of other functional genes.

The transcription factor p53: Not a repressor, solely an activator

PubMed Central

Fischer, Martin; Steiner, Lydia; Engeland, Kurt

2014-01-01

The predominant function of the tumor suppressor p53 is transcriptional regulation. It is generally accepted that p53-dependent transcriptional activation occurs by binding to a specific recognition site in promoters of target genes. Additionally, several models for p53-dependent transcriptional repression have been postulated. Here, we evaluate these models based on a computational meta-analysis of genome-wide data. Surprisingly, several major models of p53-dependent gene regulation are implausible. Meta-analysis of large-scale data is unable to confirm reports on directly repressed p53 target genes and falsifies models of direct repression. This notion is supported by experimental re-analysis of representative genes reported as directly repressed by p53. Therefore, p53 is not a direct repressor of transcription, but solely activates its target genes. Moreover, models based on interference of p53 with activating transcription factors as well as models based on the function of ncRNAs are also not supported by the meta-analysis. As an alternative to models of direct repression, the meta-analysis leads to the conclusion that p53 represses transcription indirectly by activation of the p53-p21-DREAM/RB pathway. PMID:25486564

EDGE3: A web-based solution for management and analysis of Agilent two color microarray experiments

PubMed Central

Vollrath, Aaron L; Smith, Adam A; Craven, Mark; Bradfield, Christopher A

2009-01-01

Background The ability to generate transcriptional data on the scale of entire genomes has been a boon both in the improvement of biological understanding and in the amount of data generated. The latter, the amount of data generated, has implications when it comes to effective storage, analysis and sharing of these data. A number of software tools have been developed to store, analyze, and share microarray data. However, a majority of these tools do not offer all of these features nor do they specifically target the commonly used two color Agilent DNA microarray platform. Thus, the motivating factor for the development of EDGE3 was to incorporate the storage, analysis and sharing of microarray data in a manner that would provide a means for research groups to collaborate on Agilent-based microarray experiments without a large investment in software-related expenditures or extensive training of end-users. Results EDGE3 has been developed with two major functions in mind. The first function is to provide a workflow process for the generation of microarray data by a research laboratory or a microarray facility. The second is to store, analyze, and share microarray data in a manner that doesn't require complicated software. To satisfy the first function, EDGE3 has been developed as a means to establish a well defined experimental workflow and information system for microarray generation. To satisfy the second function, the software application utilized as the user interface of EDGE3 is a web browser. Within the web browser, a user is able to access the entire functionality, including, but not limited to, the ability to perform a number of bioinformatics based analyses, collaborate between research groups through a user-based security model, and access to the raw data files and quality control files generated by the software used to extract the signals from an array image. Conclusion Here, we present EDGE3, an open-source, web-based application that allows for the storage, analysis, and controlled sharing of transcription-based microarray data generated on the Agilent DNA platform. In addition, EDGE3 provides a means for managing RNA samples and arrays during the hybridization process. EDGE3 is freely available for download at . PMID:19732451

EDGE(3): a web-based solution for management and analysis of Agilent two color microarray experiments.

PubMed

Vollrath, Aaron L; Smith, Adam A; Craven, Mark; Bradfield, Christopher A

2009-09-04

The ability to generate transcriptional data on the scale of entire genomes has been a boon both in the improvement of biological understanding and in the amount of data generated. The latter, the amount of data generated, has implications when it comes to effective storage, analysis and sharing of these data. A number of software tools have been developed to store, analyze, and share microarray data. However, a majority of these tools do not offer all of these features nor do they specifically target the commonly used two color Agilent DNA microarray platform. Thus, the motivating factor for the development of EDGE(3) was to incorporate the storage, analysis and sharing of microarray data in a manner that would provide a means for research groups to collaborate on Agilent-based microarray experiments without a large investment in software-related expenditures or extensive training of end-users. EDGE(3) has been developed with two major functions in mind. The first function is to provide a workflow process for the generation of microarray data by a research laboratory or a microarray facility. The second is to store, analyze, and share microarray data in a manner that doesn't require complicated software. To satisfy the first function, EDGE3 has been developed as a means to establish a well defined experimental workflow and information system for microarray generation. To satisfy the second function, the software application utilized as the user interface of EDGE(3) is a web browser. Within the web browser, a user is able to access the entire functionality, including, but not limited to, the ability to perform a number of bioinformatics based analyses, collaborate between research groups through a user-based security model, and access to the raw data files and quality control files generated by the software used to extract the signals from an array image. Here, we present EDGE(3), an open-source, web-based application that allows for the storage, analysis, and controlled sharing of transcription-based microarray data generated on the Agilent DNA platform. In addition, EDGE(3) provides a means for managing RNA samples and arrays during the hybridization process. EDGE(3) is freely available for download at http://edge.oncology.wisc.edu/.

AUTOMATED ANALYSIS OF QUANTITATIVE IMAGE DATA USING ISOMORPHIC FUNCTIONAL MIXED MODELS, WITH APPLICATION TO PROTEOMICS DATA.

PubMed

Morris, Jeffrey S; Baladandayuthapani, Veerabhadran; Herrick, Richard C; Sanna, Pietro; Gutstein, Howard

2011-01-01

Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper, we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate. Although the method we present is general and can be applied to quantitative image data from any application, in this paper we focus on image-based proteomic data. We apply our method to an animal study investigating the effects of opiate addiction on the brain proteome. Our image-based functional mixed model approach finds results that are missed with conventional spot-based analysis approaches. In particular, we find that the significant regions of the image identified by the proposed method frequently correspond to subregions of visible spots that may represent post-translational modifications or co-migrating proteins that cannot be visually resolved from adjacent, more abundant proteins on the gel image. Thus, it is possible that this image-based approach may actually improve the realized resolution of the gel, revealing differentially expressed proteins that would not have even been detected as spots by modern spot-based analyses.

Likelihood ratio meta-analysis: New motivation and approach for an old method.

PubMed

Dormuth, Colin R; Filion, Kristian B; Platt, Robert W

2016-03-01

A 95% confidence interval (CI) in an updated meta-analysis may not have the expected 95% coverage. If a meta-analysis is simply updated with additional data, then the resulting 95% CI will be wrong because it will not have accounted for the fact that the earlier meta-analysis failed or succeeded to exclude the null. This situation can be avoided by using the likelihood ratio (LR) as a measure of evidence that does not depend on type-1 error. We show how an LR-based approach, first advanced by Goodman, can be used in a meta-analysis to pool data from separate studies to quantitatively assess where the total evidence points. The method works by estimating the log-likelihood ratio (LogLR) function from each study. Those functions are then summed to obtain a combined function, which is then used to retrieve the total effect estimate, and a corresponding 'intrinsic' confidence interval. Using as illustrations the CAPRIE trial of clopidogrel versus aspirin in the prevention of ischemic events, and our own meta-analysis of higher potency statins and the risk of acute kidney injury, we show that the LR-based method yields the same point estimate as the traditional analysis, but with an intrinsic confidence interval that is appropriately wider than the traditional 95% CI. The LR-based method can be used to conduct both fixed effect and random effects meta-analyses, it can be applied to old and new meta-analyses alike, and results can be presented in a format that is familiar to a meta-analytic audience. Copyright © 2016 Elsevier Inc. All rights reserved.

A knowledge base for Vitis vinifera functional analysis.

PubMed

Pulvirenti, Alfredo; Giugno, Rosalba; Distefano, Rosario; Pigola, Giuseppe; Mongiovi, Misael; Giudice, Girolamo; Vendramin, Vera; Lombardo, Alessandro; Cattonaro, Federica; Ferro, Alfredo

2015-01-01

Vitis vinifera (Grapevine) is the most important fruit species in the modern world. Wine and table grapes sales contribute significantly to the economy of major wine producing countries. The most relevant goals in wine production concern quality and safety. In order to significantly improve the achievement of these objectives and to gain biological knowledge about cultivars, a genomic approach is the most reliable strategy. The recent grapevine genome sequencing offers the opportunity to study the potential roles of genes and microRNAs in fruit maturation and other physiological and pathological processes. Although several systems allowing the analysis of plant genomes have been reported, none of them has been designed specifically for the functional analysis of grapevine genomes of cultivars under environmental stress in connection with microRNA data. Here we introduce a novel knowledge base, called BIOWINE, designed for the functional analysis of Vitis vinifera genomes of cultivars present in Sicily. The system allows the analysis of RNA-seq experiments of two different cultivars, namely Nero d'Avola and Nerello Mascalese. Samples were taken under different climatic conditions of phenological phases, diseases, and geographic locations. The BIOWINE web interface is equipped with data analysis modules for grapevine genomes. In particular users may analyze the current genome assembly together with the RNA-seq data through a customized version of GBrowse. The web interface allows users to perform gene set enrichment by exploiting third-party databases. BIOWINE is a knowledge base implementing a set of bioinformatics tools for the analysis of grapevine genomes. The system aims to increase our understanding of the grapevine varieties and species of Sicilian products focusing on adaptability to different climatic conditions, phenological phases, diseases, and geographic locations.

Examining the effectiveness of discriminant function analysis and cluster analysis in species identification of male field crickets based on their calling songs.

PubMed

Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini

2013-01-01

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

Fusing modeling techniques to support domain analysis for reuse opportunities identification

NASA Technical Reports Server (NTRS)

Hall, Susan Main; Mcguire, Eileen

1993-01-01

Functional modeling techniques or object-oriented graphical representations, which are more useful to someone trying to understand the general design or high level requirements of a system? For a recent domain analysis effort, the answer was a fusion of popular modeling techniques of both types. By using both functional and object-oriented techniques, the analysts involved were able to lean on their experience in function oriented software development, while taking advantage of the descriptive power available in object oriented models. In addition, a base of familiar modeling methods permitted the group of mostly new domain analysts to learn the details of the domain analysis process while producing a quality product. This paper describes the background of this project and then provides a high level definition of domain analysis. The majority of this paper focuses on the modeling method developed and utilized during this analysis effort.

Field-based Metabolomics for Assessing Contaminated Surface Waters

EPA Science Inventory

Metabolomics is becoming well-established for studying chemical contaminant-induced alterations to normal biological function. For example, the literature contains a wealth of laboratory-based studies involving analysis of samples from organisms exposed to individual chemical tox...

FPGA implementation of motifs-based neuronal network and synchronization analysis

NASA Astrophysics Data System (ADS)

Deng, Bin; Zhu, Zechen; Yang, Shuangming; Wei, Xile; Wang, Jiang; Yu, Haitao

2016-06-01

Motifs in complex networks play a crucial role in determining the brain functions. In this paper, 13 kinds of motifs are implemented with Field Programmable Gate Array (FPGA) to investigate the relationships between the networks properties and motifs properties. We use discretization method and pipelined architecture to construct various motifs with Hindmarsh-Rose (HR) neuron as the node model. We also build a small-world network based on these motifs and conduct the synchronization analysis of motifs as well as the constructed network. We find that the synchronization properties of motif determine that of motif-based small-world network, which demonstrates effectiveness of our proposed hardware simulation platform. By imitation of some vital nuclei in the brain to generate normal discharges, our proposed FPGA-based artificial neuronal networks have the potential to replace the injured nuclei to complete the brain function in the treatment of Parkinson's disease and epilepsy.

A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines.

PubMed

Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

2016-12-13

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods.

A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines

PubMed Central

Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

2016-01-01

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods. PMID:27983577

The Information Content of Discrete Functions and Their Application in Genetic Data Analysis

DOE PAGES

Sakhanenko, Nikita A.; Kunert-Graf, James; Galas, David J.

2017-10-13

The complex of central problems in data analysis consists of three components: (1) detecting the dependence of variables using quantitative measures, (2) defining the significance of these dependence measures, and (3) inferring the functional relationships among dependent variables. We have argued previously that an information theory approach allows separation of the detection problem from the inference of functional form problem. We approach here the third component of inferring functional forms based on information encoded in the functions. Here, we present here a direct method for classifying the functional forms of discrete functions of three variables represented in data sets. Discretemore » variables are frequently encountered in data analysis, both as the result of inherently categorical variables and from the binning of continuous numerical variables into discrete alphabets of values. The fundamental question of how much information is contained in a given function is answered for these discrete functions, and their surprisingly complex relationships are illustrated. The all-important effect of noise on the inference of function classes is found to be highly heterogeneous and reveals some unexpected patterns. We apply this classification approach to an important area of biological data analysis—that of inference of genetic interactions. Genetic analysis provides a rich source of real and complex biological data analysis problems, and our general methods provide an analytical basis and tools for characterizing genetic problems and for analyzing genetic data. Finally, we illustrate the functional description and the classes of a number of common genetic interaction modes and also show how different modes vary widely in their sensitivity to noise.« less

The Information Content of Discrete Functions and Their Application in Genetic Data Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sakhanenko, Nikita A.; Kunert-Graf, James; Galas, David J.

The complex of central problems in data analysis consists of three components: (1) detecting the dependence of variables using quantitative measures, (2) defining the significance of these dependence measures, and (3) inferring the functional relationships among dependent variables. We have argued previously that an information theory approach allows separation of the detection problem from the inference of functional form problem. We approach here the third component of inferring functional forms based on information encoded in the functions. Here, we present here a direct method for classifying the functional forms of discrete functions of three variables represented in data sets. Discretemore » variables are frequently encountered in data analysis, both as the result of inherently categorical variables and from the binning of continuous numerical variables into discrete alphabets of values. The fundamental question of how much information is contained in a given function is answered for these discrete functions, and their surprisingly complex relationships are illustrated. The all-important effect of noise on the inference of function classes is found to be highly heterogeneous and reveals some unexpected patterns. We apply this classification approach to an important area of biological data analysis—that of inference of genetic interactions. Genetic analysis provides a rich source of real and complex biological data analysis problems, and our general methods provide an analytical basis and tools for characterizing genetic problems and for analyzing genetic data. Finally, we illustrate the functional description and the classes of a number of common genetic interaction modes and also show how different modes vary widely in their sensitivity to noise.« less

Functional proteomic analysis revealed ground-base ion radiations cannot reflect biological effects of space radiations of rice

NASA Astrophysics Data System (ADS)

Wang, Wei; Sun, Yeqing; Zhao, Qian; Han, Lu

2016-07-01

Highly ionizing radiation (HZE) in space is considered as main factor causing biological effects. Radiobiological studies during space flights are unrepeatable due to the variable space radiation environment, ground-base ion radiations are usually performed to simulate of the space biological effect. Spaceflights present a low-dose rate (0.1˜~0.3mGy/day) radiation environment inside aerocrafts while ground-base ion radiations present a much higher dose rate (100˜~500mGy/min). Whether ground-base ion radiation can reflect effects of space radiation is worth of evaluation. In this research, we compared the functional proteomic profiles of rice plants between on-ground simulated HZE particle radiation and spaceflight treatments. Three independent ground-base seed ionizing radiation experiments with different cumulative doses (dose range: 2˜~20000mGy) and different liner energy transfer (LET) values (13.3˜~500keV/μμm) and two independent seed spaceflight experiments onboard Chinese 20th satellite and SZ-6 spacecraft were carried out. Alterations in the proteome were analyzed by two-dimensional difference gel electrophoresis (2-D DIGE) with MALDI-TOF/TOF mass spectrometry identifications. 45 and 59 proteins showed significant (p<0.05) and reproducible quantitative differences in ground-base ion radiation and spaceflight experiments respectively. The functions of ground-base radiation and spaceflight proteins were both involved in a wide range of biological processes. Gene Ontology enrichment analysis further revealed that ground-base radiation responsive proteins were mainly involved in removal of superoxide radicals, defense response to stimulus and photosynthesis, while spaceflight responsive proteins mainly participate in nucleoside metabolic process, protein folding and phosphorylation. The results implied that ground-base radiations cannot truly reflect effects of spaceflight radiations, ground-base radiation was a kind of indirect effect to rice causing oxidation and metabolism stresses, but space radiation was a kind of direct effect leading to macromolecule (DNA and protein) damage and signal pathway disorders. This functional proteomic analysis work might provide a new evaluation method for further on-ground simulated HZE radiation experiments.

Base-Rate Neglect as a Function of Base Rates in Probabilistic Contingency Learning

ERIC Educational Resources Information Center

Kutzner, Florian; Freytag, Peter; Vogel, Tobias; Fiedler, Klaus

2008-01-01

When humans predict criterion events based on probabilistic predictors, they often lend excessive weight to the predictor and insufficient weight to the base rate of the criterion event. In an operant analysis, using a matching-to-sample paradigm, Goodie and Fantino (1996) showed that humans exhibit base-rate neglect when predictors are associated…

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

PubMed

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

[Fine mapping of complex disease susceptibility loci].

PubMed

Song, Qingfeng; Zhang, Hongxing; Ma, Yilong; Zhou, Gangqiao

2014-01-01

Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers have identified more than 3800 susceptibility loci for more than 660 diseases or traits. However, the most significantly associated variants or causative variants in these loci and their biological functions have remained to be clarified. These causative variants can help to elucidate the pathogenesis and discover new biomarkers of complex diseases. One of the main goals in the post-GWAS era is to identify the causative variants and susceptibility genes, and clarify their functional aspects by fine mapping. For common variants, imputation or re-sequencing based strategies were implemented to increase the number of analyzed variants and help to identify the most significantly associated variants. In addition, functional element, expression quantitative trait locus (eQTL) and haplotype analyses were performed to identify functional common variants and susceptibility genes. For rare variants, fine mapping was carried out by re-sequencing, rare haplotype analysis, family-based analysis, burden test, etc.This review summarizes the strategies and problems for fine mapping.

Neuroimaging in epilepsy.

PubMed

Sidhu, Meneka Kaur; Duncan, John S; Sander, Josemir W

2018-05-17

Epilepsy neuroimaging is important for detecting the seizure onset zone, predicting and preventing deficits from surgery and illuminating mechanisms of epileptogenesis. An aspiration is to integrate imaging and genetic biomarkers to enable personalized epilepsy treatments. The ability to detect lesions, particularly focal cortical dysplasia and hippocampal sclerosis, is increased using ultra high-field imaging and postprocessing techniques such as automated volumetry, T2 relaxometry, voxel-based morphometry and surface-based techniques. Statistical analysis of PET and single photon emission computer tomography (STATISCOM) are superior to qualitative analysis alone in identifying focal abnormalities in MRI-negative patients. These methods have also been used to study mechanisms of epileptogenesis and pharmacoresistance.Recent language fMRI studies aim to localize, and also lateralize language functions. Memory fMRI has been recommended to lateralize mnemonic function and predict outcome after surgery in temporal lobe epilepsy. Combinations of structural, functional and post-processing methods have been used in multimodal and machine learning models to improve the identification of the seizure onset zone and increase understanding of mechanisms underlying structural and functional aberrations in epilepsy.

Analysis of earing behaviour in deep drawing of ASS 304 at elevated temperature

NASA Astrophysics Data System (ADS)

Gupta, Amit Kumar; Deole, Aditya; Kotkunde, Nitin; Singh, Swadesh Kumar; jella, Gangadhar

2016-08-01

Earing tendency in a deep drawn cup of circular blanks is one the most prominent characteristics observed due to anisotropy in a metal sheet. Such formation of uneven rim is mainly due to dissimilarity in yield stress as well as Lankford parameter (r- value) in different orientations. In this paper, an analytical function coupled with different yield functions viz., Hill 1948, Barlat 1989 and Barlat Yld 2000-2d has been used to provide an approximation of earing profile. In order to validate the results, material parameters for yield functions and hardening rule have been calibrated for ASS 304 at 250°C and deep drawing experiment is conducted to measure the earing profile. The predicted earing profiles based on analytical results have been validated using experimental earing profile. Based on this analysis, Barlat Yld 2000-2d has been observed to be a well suited yield model for deep drawing of ASS 304, which also confirms the reliability of analytical function for earing profile estimation.

Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics.

PubMed

de Angelis, Martin Hrabě; Nicholson, George; Selloum, Mohammed; White, Jacqui; Morgan, Hugh; Ramirez-Solis, Ramiro; Sorg, Tania; Wells, Sara; Fuchs, Helmut; Fray, Martin; Adams, David J; Adams, Niels C; Adler, Thure; Aguilar-Pimentel, Antonio; Ali-Hadji, Dalila; Amann, Gregory; André, Philippe; Atkins, Sarah; Auburtin, Aurelie; Ayadi, Abdel; Becker, Julien; Becker, Lore; Bedu, Elodie; Bekeredjian, Raffi; Birling, Marie-Christine; Blake, Andrew; Bottomley, Joanna; Bowl, Mike; Brault, Véronique; Busch, Dirk H; Bussell, James N; Calzada-Wack, Julia; Cater, Heather; Champy, Marie-France; Charles, Philippe; Chevalier, Claire; Chiani, Francesco; Codner, Gemma F; Combe, Roy; Cox, Roger; Dalloneau, Emilie; Dierich, André; Di Fenza, Armida; Doe, Brendan; Duchon, Arnaud; Eickelberg, Oliver; Esapa, Chris T; El Fertak, Lahcen; Feigel, Tanja; Emelyanova, Irina; Estabel, Jeanne; Favor, Jack; Flenniken, Ann; Gambadoro, Alessia; Garrett, Lilian; Gates, Hilary; Gerdin, Anna-Karin; Gkoutos, George; Greenaway, Simon; Glasl, Lisa; Goetz, Patrice; Da Cruz, Isabelle Goncalves; Götz, Alexander; Graw, Jochen; Guimond, Alain; Hans, Wolfgang; Hicks, Geoff; Hölter, Sabine M; Höfler, Heinz; Hancock, John M; Hoehndorf, Robert; Hough, Tertius; Houghton, Richard; Hurt, Anja; Ivandic, Boris; Jacobs, Hughes; Jacquot, Sylvie; Jones, Nora; Karp, Natasha A; Katus, Hugo A; Kitchen, Sharon; Klein-Rodewald, Tanja; Klingenspor, Martin; Klopstock, Thomas; Lalanne, Valerie; Leblanc, Sophie; Lengger, Christoph; le Marchand, Elise; Ludwig, Tonia; Lux, Aline; McKerlie, Colin; Maier, Holger; Mandel, Jean-Louis; Marschall, Susan; Mark, Manuel; Melvin, David G; Meziane, Hamid; Micklich, Kateryna; Mittelhauser, Christophe; Monassier, Laurent; Moulaert, David; Muller, Stéphanie; Naton, Beatrix; Neff, Frauke; Nolan, Patrick M; Nutter, Lauryl Mj; Ollert, Markus; Pavlovic, Guillaume; Pellegata, Natalia S; Peter, Emilie; Petit-Demoulière, Benoit; Pickard, Amanda; Podrini, Christine; Potter, Paul; Pouilly, Laurent; Puk, Oliver; Richardson, David; Rousseau, Stephane; Quintanilla-Fend, Leticia; Quwailid, Mohamed M; Racz, Ildiko; Rathkolb, Birgit; Riet, Fabrice; Rossant, Janet; Roux, Michel; Rozman, Jan; Ryder, Ed; Salisbury, Jennifer; Santos, Luis; Schäble, Karl-Heinz; Schiller, Evelyn; Schrewe, Anja; Schulz, Holger; Steinkamp, Ralf; Simon, Michelle; Stewart, Michelle; Stöger, Claudia; Stöger, Tobias; Sun, Minxuan; Sunter, David; Teboul, Lydia; Tilly, Isabelle; Tocchini-Valentini, Glauco P; Tost, Monica; Treise, Irina; Vasseur, Laurent; Velot, Emilie; Vogt-Weisenhorn, Daniela; Wagner, Christelle; Walling, Alison; Weber, Bruno; Wendling, Olivia; Westerberg, Henrik; Willershäuser, Monja; Wolf, Eckhard; Wolter, Anne; Wood, Joe; Wurst, Wolfgang; Yildirim, Ali Önder; Zeh, Ramona; Zimmer, Andreas; Zimprich, Annemarie; Holmes, Chris; Steel, Karen P; Herault, Yann; Gailus-Durner, Valérie; Mallon, Ann-Marie; Brown, Steve Dm

2015-09-01

The function of the majority of genes in the mouse and human genomes remains unknown. The mouse embryonic stem cell knockout resource provides a basis for the characterization of relationships between genes and phenotypes. The EUMODIC consortium developed and validated robust methodologies for the broad-based phenotyping of knockouts through a pipeline comprising 20 disease-oriented platforms. We developed new statistical methods for pipeline design and data analysis aimed at detecting reproducible phenotypes with high power. We acquired phenotype data from 449 mutant alleles, representing 320 unique genes, of which half had no previous functional annotation. We captured data from over 27,000 mice, finding that 83% of the mutant lines are phenodeviant, with 65% demonstrating pleiotropy. Surprisingly, we found significant differences in phenotype annotation according to zygosity. New phenotypes were uncovered for many genes with previously unknown function, providing a powerful basis for hypothesis generation and further investigation in diverse systems.

Determination of the authenticity of plastron-derived functional foods based on amino acid profiles analysed by MEKC.

PubMed

Li, Lin-Qiu; Baibado, Joewel T; Shen, Qing; Cheung, Hon-Yeung

2017-12-01

Plastron is a nutritive and superior functional food. Due to its limited supply yet enormous demands, some functional foods supposed to contain plastron may be forged with other substitutes. This paper reports a novel and simple method for determination of the authenticity of plastron-derived functional foods based on comparison of the amino acid (AA) profiles of plastron and its possible substitutes. By applying micellar electrokinetic chromatography (MEKC), 18 common AAs along with another 2 special AAs - hydroxyproline (Hyp) and hydroxylysine (Hyl) were detected in all plastron samples. Since chicken, egg, fish, milk, pork, nail and hair lacked of Hyp and Hyl, plastron could be easily distinguished. For those containing collagen, a statistical analysis technique - principal component analysis (PCA) was adopted and plastron was successfully distinguished. When applied the proposed method to authenticate turtle shell glue in the market, fake products were commonly found. Copyright © 2017 Elsevier B.V. All rights reserved.

Functional specializations in human cerebral cortex analyzed using the Visible Man surface-based atlas

NASA Technical Reports Server (NTRS)

Drury, H. A.; Van Essen, D. C.

1997-01-01

We used surface-based representations to analyze functional specializations in the human cerebral cortex. A computerized reconstruction of the cortical surface of the Visible Man digital atlas was generated and transformed to the Talairach coordinate system. This surface was also flattened and used to establish a surface-based coordinate system that respects the topology of the cortical sheet. The linkage between two-dimensional and three-dimensional representations allows the locations of published neuroimaging activation foci to be stereotaxically projected onto the Visible Man cortical flat map. An analysis of two activation studies related to the hearing and reading of music and of words illustrates how this approach permits the systematic estimation of the degree of functional segregation and of potential functional overlap for different aspects of sensory processing.

LMI-based stability analysis of fuzzy-model-based control systems using approximated polynomial membership functions.

PubMed

Narimani, Mohammand; Lam, H K; Dilmaghani, R; Wolfe, Charles

2011-06-01

Relaxed linear-matrix-inequality-based stability conditions for fuzzy-model-based control systems with imperfect premise matching are proposed. First, the derivative of the Lyapunov function, containing the product terms of the fuzzy model and fuzzy controller membership functions, is derived. Then, in the partitioned operating domain of the membership functions, the relations between the state variables and the mentioned product terms are represented by approximated polynomials in each subregion. Next, the stability conditions containing the information of all subsystems and the approximated polynomials are derived. In addition, the concept of the S-procedure is utilized to release the conservativeness caused by considering the whole operating region for approximated polynomials. It is shown that the well-known stability conditions can be special cases of the proposed stability conditions. Simulation examples are given to illustrate the validity of the proposed approach.

A clustering-based graph Laplacian framework for value function approximation in reinforcement learning.

PubMed

Xu, Xin; Huang, Zhenhua; Graves, Daniel; Pedrycz, Witold

2014-12-01

In order to deal with the sequential decision problems with large or continuous state spaces, feature representation and function approximation have been a major research topic in reinforcement learning (RL). In this paper, a clustering-based graph Laplacian framework is presented for feature representation and value function approximation (VFA) in RL. By making use of clustering-based techniques, that is, K-means clustering or fuzzy C-means clustering, a graph Laplacian is constructed by subsampling in Markov decision processes (MDPs) with continuous state spaces. The basis functions for VFA can be automatically generated from spectral analysis of the graph Laplacian. The clustering-based graph Laplacian is integrated with a class of approximation policy iteration algorithms called representation policy iteration (RPI) for RL in MDPs with continuous state spaces. Simulation and experimental results show that, compared with previous RPI methods, the proposed approach needs fewer sample points to compute an efficient set of basis functions and the learning control performance can be improved for a variety of parameter settings.

ESTABLISHING VERBAL REPERTOIRES IN CHILDREN WITH AUTISM USING FUNCTION-BASED VIDEO MODELING

PubMed Central

Plavnick, Joshua B; Ferreri, Summer J

2011-01-01

Previous research suggests that language-training procedures for children with autism might be enhanced following an assessment of conditions that evoke emerging verbal behavior. The present investigation examined a methodology to teach recognizable mands based on environmental variables known to evoke participants' idiosyncratic communicative responses in the natural environment. An alternating treatments design was used during Experiment 1 to identify the variables that were functionally related to gestures emitted by 4 children with autism. Results showed that gestures functioned as requests for attention for 1 participant and as requests for assistance to obtain a preferred item or event for 3 participants. Video modeling was used during Experiment 2 to compare mand acquisition when video sequences were either related or unrelated to the results of the functional analysis. An alternating treatments within multiple probe design showed that participants repeatedly acquired mands during the function-based condition but not during the nonfunction-based condition. In addition, generalization of the response was observed during the former but not the latter condition. PMID:22219527

Establishing verbal repertoires in children with autism using function-based video modeling.

PubMed

Plavnick, Joshua B; Ferreri, Summer J

2011-01-01

Previous research suggests that language-training procedures for children with autism might be enhanced following an assessment of conditions that evoke emerging verbal behavior. The present investigation examined a methodology to teach recognizable mands based on environmental variables known to evoke participants' idiosyncratic communicative responses in the natural environment. An alternating treatments design was used during Experiment 1 to identify the variables that were functionally related to gestures emitted by 4 children with autism. Results showed that gestures functioned as requests for attention for 1 participant and as requests for assistance to obtain a preferred item or event for 3 participants. Video modeling was used during Experiment 2 to compare mand acquisition when video sequences were either related or unrelated to the results of the functional analysis. An alternating treatments within multiple probe design showed that participants repeatedly acquired mands during the function-based condition but not during the nonfunction-based condition. In addition, generalization of the response was observed during the former but not the latter condition.

Monitoring of human brain functions in risk decision-making task by diffuse optical tomography using voxel-wise general linear model

NASA Astrophysics Data System (ADS)

Lin, Zi-Jing; Li, Lin; Cazzell, Marry; Liu, Hanli

2013-03-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique which measures the hemodynamic changes that reflect the brain activity. Diffuse optical tomography (DOT), a variant of fNIRS with multi-channel NIRS measurements, has demonstrated capability of three dimensional (3D) reconstructions of hemodynamic changes due to the brain activity. Conventional method of DOT image analysis to define the brain activation is based upon the paired t-test between two different states, such as resting-state versus task-state. However, it has limitation because the selection of activation and post-activation period is relatively subjective. General linear model (GLM) based analysis can overcome this limitation. In this study, we combine the 3D DOT image reconstruction with GLM-based analysis (i.e., voxel-wise GLM analysis) to investigate the brain activity that is associated with the risk-decision making process. Risk decision-making is an important cognitive process and thus is an essential topic in the field of neuroscience. The balloon analogue risk task (BART) is a valid experimental model and has been commonly used in behavioral measures to assess human risk taking action and tendency while facing risks. We have utilized the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making. Voxel-wise GLM analysis was performed on 18human participants (10 males and 8females).In this work, we wish to demonstrate the feasibility of using voxel-wise GLM analysis to image and study cognitive functions in response to risk decision making by DOT. Results have shown significant changes in the dorsal lateral prefrontal cortex (DLPFC) during the active choice mode and a different hemodynamic pattern between genders, which are in good agreements with published literatures in functional magnetic resonance imaging (fMRI) and fNIRS studies.

Framework Design and Influencing Factor Analysis of a Water Environmental Functional Zone-Based Effluent Trading System.

PubMed

Chen, Lei; Han, Zhaoxing; Li, Shuang; Shen, Zhenyao

2016-10-01

The efficacy of traditional effluent trading systems is questionable due to their neglect of seasonal hydrological variation and the creation of upstream hot spots within a watershed. Besides, few studies have been conducted to distinguish the impacts of each influencing factor on effluent trading systems outputs. In this study, a water environmental functional zone-based effluent trading systems framework was configured and a comprehensive analysis of its influencing factors was conducted. This proposed water environmental functional zone-based effluent trading systems was then applied for the control of chemical oxygen demand in the Beiyun River watershed, Beijing, China. Optimal trading results highlighted the integration of water quality constraints and different hydrological seasons, especially for downstream dischargers. The optimal trading of each discharger, in terms of pollutant reduction load and abatement cost, is greatly influenced by environmental and political factors such as background water quality, the location of river assessment points, and tradable discharge permits. In addition, the initial permit allowance has little influence on the market as a whole but does impact the individual discharger. These results provide information that is critical to understanding the impact of policy design on the functionality of an effluent trading systems.

Free-edge effects in laminates under extension, bending and twisting. II - Sublaminate/layer modeling and analysis

NASA Technical Reports Server (NTRS)

Yin, Wan-Lee

1992-01-01

The stress-function-based variational method of Yin (1991) is extended and modified into a combined layer/sublaminate approach applicable to a laminated strip composed of a large number of differently orientated, anisotropic elastic plies. Lekhnitskii's (1963) stress functions are introduced into two interior layers adjacent to a particular interface. The remaining layers are grouped into an upper sublaminate and a lower sublaminate. The stress functions are expanded in truncated power series of the thickness coordinate, and the differential equations governing the coefficient functions are derived by using the complementary virtual work principle. The layer/sublaminate approach limits the dimension of the eigenvalue problem to a fixed number irrespective of the number of layers in the sublaminate, so that reasonably accurate solutions of the interlaminar stresses can be computed with extreme ease. For symmetric, four-layer, angle-ply and cross-ply laminates, a comparison of the previous analysis results based on the pure layer model and new results based on two different layer/sublaminate models indicates reasonable over-all agreement in the interlaminar stresses and superior agreement in the total peeling and shearing force.

Framework Design and Influencing Factor Analysis of a Water Environmental Functional Zone-Based Effluent Trading System

NASA Astrophysics Data System (ADS)

Chen, Lei; Han, Zhaoxing; Li, Shuang; Shen, Zhenyao

2016-10-01

The efficacy of traditional effluent trading systems is questionable due to their neglect of seasonal hydrological variation and the creation of upstream hot spots within a watershed. Besides, few studies have been conducted to distinguish the impacts of each influencing factor on effluent trading systems outputs. In this study, a water environmental functional zone-based effluent trading systems framework was configured and a comprehensive analysis of its influencing factors was conducted. This proposed water environmental functional zone-based effluent trading systems was then applied for the control of chemical oxygen demand in the Beiyun River watershed, Beijing, China. Optimal trading results highlighted the integration of water quality constraints and different hydrological seasons, especially for downstream dischargers. The optimal trading of each discharger, in terms of pollutant reduction load and abatement cost, is greatly influenced by environmental and political factors such as background water quality, the location of river assessment points, and tradable discharge permits. In addition, the initial permit allowance has little influence on the market as a whole but does impact the individual discharger. These results provide information that is critical to understanding the impact of policy design on the functionality of an effluent trading systems.

A Modified ABCDE Model of Flowering in Orchids Based on Gene Expression Profiling Studies of the Moth Orchid Phalaenopsis aphrodite

PubMed Central

Lee, Ann-Ying; Chen, Chun-Yi; Chang, Yao-Chien Alex; Chao, Ya-Ting; Shih, Ming-Che

2013-01-01

Previously we developed genomic resources for orchids, including transcriptomic analyses using next-generation sequencing techniques and construction of a web-based orchid genomic database. Here, we report a modified molecular model of flower development in the Orchidaceae based on functional analysis of gene expression profiles in Phalaenopsis aphrodite (a moth orchid) that revealed novel roles for the transcription factors involved in floral organ pattern formation. Phalaenopsis orchid floral organ-specific genes were identified by microarray analysis. Several critical transcription factors including AP3, PI, AP1 and AGL6, displayed distinct spatial distribution patterns. Phylogenetic analysis of orchid MADS box genes was conducted to infer the evolutionary relationship among floral organ-specific genes. The results suggest that gene duplication MADS box genes in orchid may have resulted in their gaining novel functions during evolution. Based on these analyses, a modified model of orchid flowering was proposed. Comparison of the expression profiles of flowers of a peloric mutant and wild-type Phalaenopsis orchid further identified genes associated with lip morphology and peloric effects. Large scale investigation of gene expression profiles revealed that homeotic genes from the ABCDE model of flower development classes A and B in the Phalaenopsis orchid have novel functions due to evolutionary diversification, and display differential expression patterns. PMID:24265826

[Construction and application of special analysis database of geoherbs based on 3S technology].

PubMed

Guo, Lan-ping; Huang, Lu-qi; Lv, Dong-mei; Shao, Ai-juan; Wang, Jian

2007-09-01

In this paper,the structures, data sources, data codes of "the spacial analysis database of geoherbs" based 3S technology are introduced, and the essential functions of the database, such as data management, remote sensing, spacial interpolation, spacial statistics, spacial analysis and developing are described. At last, two examples for database usage are given, the one is classification and calculating of NDVI index of remote sensing image in geoherbal area of Atractylodes lancea, the other one is adaptation analysis of A. lancea. These indicate that "the spacial analysis database of geoherbs" has bright prospect in spacial analysis of geoherbs.

An Analysis of Grade 11 Learners' Levels of Understanding of Functions in Terms of APOS Theory

ERIC Educational Resources Information Center

Chimhande, Tinoda; Naidoo, Ana; Stols, Gerrit

2017-01-01

This article reports on a study of six Grade 11 learners' levels of understanding of concepts related to the function definition and representation. Task-based clinical interviews were used to elicit the learners' interpretations and reasoning when working with these function-related concepts. Indicators for Action-Process-Object-Schema (APOS)…

Using Analogue Functional Analysis to Measure Variations in Problem Behavior Rate and Function after Psychotropic Medication Changes: A Clinical Demonstration

ERIC Educational Resources Information Center

Valdovinos, Maria G.; Nelson, Samantha M.; Kuhle, Jennifer L.; Dierks, Abigail M.

2009-01-01

Individuals with intellectual and developmental disabilities are often prescribed psychotropic medication to treat behaviors such as aggression and self-injury. Evaluation of these medications is often based on caregiver report or changes in frequency of behavior. The purpose of this research was to characterize the rate and function of problem…

Mining dynamic noteworthy functions in software execution sequences.

PubMed

Zhang, Bing; Huang, Guoyan; Wang, Yuqian; He, Haitao; Ren, Jiadong

2017-01-01

As the quality of crucial entities can directly affect that of software, their identification and protection become an important premise for effective software development, management, maintenance and testing, which thus contribute to improving the software quality and its attack-defending ability. Most analysis and evaluation on important entities like codes-based static structure analysis are on the destruction of the actual software running. In this paper, from the perspective of software execution process, we proposed an approach to mine dynamic noteworthy functions (DNFM)in software execution sequences. First, according to software decompiling and tracking stack changes, the execution traces composed of a series of function addresses were acquired. Then these traces were modeled as execution sequences and then simplified so as to get simplified sequences (SFS), followed by the extraction of patterns through pattern extraction (PE) algorithm from SFS. After that, evaluating indicators inner-importance and inter-importance were designed to measure the noteworthiness of functions in DNFM algorithm. Finally, these functions were sorted by their noteworthiness. Comparison and contrast were conducted on the experiment results from two traditional complex network-based node mining methods, namely PageRank and DegreeRank. The results show that the DNFM method can mine noteworthy functions in software effectively and precisely.

Functional brain networks in healthy subjects under acupuncture stimulation: An EEG study based on nonlinear synchronization likelihood analysis

NASA Astrophysics Data System (ADS)

Yu, Haitao; Liu, Jing; Cai, Lihui; Wang, Jiang; Cao, Yibin; Hao, Chongqing

2017-02-01

Electroencephalogram (EEG) signal evoked by acupuncture stimulation at "Zusanli" acupoint is analyzed to investigate the modulatory effect of manual acupuncture on the functional brain activity. Power spectral density of EEG signal is first calculated based on the autoregressive Burg method. It is shown that the EEG power is significantly increased during and after acupuncture in delta and theta bands, but decreased in alpha band. Furthermore, synchronization likelihood is used to estimate the nonlinear correlation between each pairwise EEG signals. By applying a threshold to resulting synchronization matrices, functional networks for each band are reconstructed and further quantitatively analyzed to study the impact of acupuncture on network structure. Graph theoretical analysis demonstrates that the functional connectivity of the brain undergoes obvious change under different conditions: pre-acupuncture, acupuncture, and post-acupuncture. The minimum path length is largely decreased and the clustering coefficient keeps increasing during and after acupuncture in delta and theta bands. It is indicated that acupuncture can significantly modulate the functional activity of the brain, and facilitate the information transmission within different brain areas. The obtained results may facilitate our understanding of the long-lasting effect of acupuncture on the brain function.

The Effect of Occupation-based Cognitive Rehabilitation for Traumatic Brain Injury: A Meta-analysis of Randomized Controlled Trials.

PubMed

Park, Hae Yean; Maitra, Kinsuk; Martinez, Kristina Marie

2015-06-01

Traumatic brain injury (TBI) is the leading cause of death and disability among people younger than 35 years in the United States. Cognitive difficulty is a common consequence of TBI. To address cognitive deficits of patients with TBI, various cognitive rehabilitation approaches have been used for the clinical setting. The purpose of this study was to investigate the overall effect of occupation-based cognitive rehabilitation on patients' improvement in cognitive performance components, activity of daily living (ADL) performance, and values, beliefs and spirituality functions of patients with TBI. The papers used in this study were retrieved from the Cochrane Database, EBSCO (CINAHL), PsycINFO, PubMed and Web of Science published between 1997 and 2014. The keywords for searching were cognitive, rehabilitation, occupation, memory, attention, problem-solving, executive function, ADL, values, beliefs, spirituality, randomized controlled trials and TBI. For the meta-analysis, we examined 60 effect sizes from nine studies that are related to the occupation-based cognitive rehabilitation on persons with TBI. In persons with TBI, overall mental functions, ADL, and values, beliefs and spirituality were significantly improved in the groups that received occupation-based cognitive rehabilitation compared with comparison groups (mean d = 0.19, p < .05). Evidence from the present meta-analytic study suggests that occupation-based cognitive rehabilitation would be beneficial for individuals with TBI for improving daily functioning and positively be able to affect their psychosocial functions. Collecting many outcome measures in studies with relatively few participants and the final data are less reliable than the whole instrument itself. Future research should evaluate the effectiveness of specific occupation-based cognitive rehabilitations programmes in order to improve consistency among rehabilitation providers. Copyright © 2015 John Wiley & Sons, Ltd.

Iodine-based contrast media, multiple myeloma and monoclonal gammopathies: literature review and ESUR Contrast Media Safety Committee guidelines.

PubMed

Stacul, Fulvio; Bertolotto, Michele; Thomsen, Henrik S; Pozzato, Gabriele; Ugolini, Donatella; Bellin, Marie-France; Bongartz, Georg; Clement, Olivier; Heinz-Peer, Gertraud; van der Molen, Aart; Reimer, Peter; Webb, Judith A W

2018-02-01

Many radiologists and clinicians still consider multiple myeloma (MM) and monoclonal gammopathies (MG) a contraindication for using iodine-based contrast media. The ESUR Contrast Media Safety Committee performed a systematic review of the incidence of post-contrast acute kidney injury (PC-AKI) in these patients. A systematic search in Medline and Scopus databases was performed for renal function deterioration studies in patients with MM or MG following administration of iodine-based contrast media. Data collection and analysis were performed according to the PRISMA statement 2009. Eligibility criteria and methods of analysis were specified in advance. Cohort and case-control studies reporting changes in renal function were included. Thirteen studies were selected that reported 824 iodine-based contrast medium administrations in 642 patients with MM or MG, in which 12 unconfounded cases of PC-AKI were found (1.6 %). The majority of patients had intravenous urography with high osmolality ionic contrast media after preparatory dehydration and purgation. MM and MG alone are not risk factors for PC-AKI. However, the risk of PC-AKI may become significant in dehydrated patients with impaired renal function. Hypercalcaemia may increase the risk of kidney damage, and should be corrected before contrast medium administration. Assessment for Bence-Jones proteinuria is not necessary. • Monoclonal gammopathies including multiple myeloma are a large spectrum of disorders. • In monoclonal gammopathy with normal renal function, PC-AKI risk is not increased. • Renal function is often reduced in myeloma, increasing the risk of PC-AKI. • Correction of hypercalcaemia is necessary in myeloma before iodine-based contrast medium administration. • Bence-Jones proteinuria assessment in myeloma is unnecessary before iodine-based contrast medium administration.

Memory-Efficient Analysis of Dense Functional Connectomes.

PubMed

Loewe, Kristian; Donohue, Sarah E; Schoenfeld, Mircea A; Kruse, Rudolf; Borgelt, Christian

2016-01-01

The functioning of the human brain relies on the interplay and integration of numerous individual units within a complex network. To identify network configurations characteristic of specific cognitive tasks or mental illnesses, functional connectomes can be constructed based on the assessment of synchronous fMRI activity at separate brain sites, and then analyzed using graph-theoretical concepts. In most previous studies, relatively coarse parcellations of the brain were used to define regions as graphical nodes. Such parcellated connectomes are highly dependent on parcellation quality because regional and functional boundaries need to be relatively consistent for the results to be interpretable. In contrast, dense connectomes are not subject to this limitation, since the parcellation inherent to the data is used to define graphical nodes, also allowing for a more detailed spatial mapping of connectivity patterns. However, dense connectomes are associated with considerable computational demands in terms of both time and memory requirements. The memory required to explicitly store dense connectomes in main memory can render their analysis infeasible, especially when considering high-resolution data or analyses across multiple subjects or conditions. Here, we present an object-based matrix representation that achieves a very low memory footprint by computing matrix elements on demand instead of explicitly storing them. In doing so, memory required for a dense connectome is reduced to the amount needed to store the underlying time series data. Based on theoretical considerations and benchmarks, different matrix object implementations and additional programs (based on available Matlab functions and Matlab-based third-party software) are compared with regard to their computational efficiency. The matrix implementation based on on-demand computations has very low memory requirements, thus enabling analyses that would be otherwise infeasible to conduct due to insufficient memory. An open source software package containing the created programs is available for download.

Memory-Efficient Analysis of Dense Functional Connectomes

PubMed Central

Loewe, Kristian; Donohue, Sarah E.; Schoenfeld, Mircea A.; Kruse, Rudolf; Borgelt, Christian

2016-01-01

The functioning of the human brain relies on the interplay and integration of numerous individual units within a complex network. To identify network configurations characteristic of specific cognitive tasks or mental illnesses, functional connectomes can be constructed based on the assessment of synchronous fMRI activity at separate brain sites, and then analyzed using graph-theoretical concepts. In most previous studies, relatively coarse parcellations of the brain were used to define regions as graphical nodes. Such parcellated connectomes are highly dependent on parcellation quality because regional and functional boundaries need to be relatively consistent for the results to be interpretable. In contrast, dense connectomes are not subject to this limitation, since the parcellation inherent to the data is used to define graphical nodes, also allowing for a more detailed spatial mapping of connectivity patterns. However, dense connectomes are associated with considerable computational demands in terms of both time and memory requirements. The memory required to explicitly store dense connectomes in main memory can render their analysis infeasible, especially when considering high-resolution data or analyses across multiple subjects or conditions. Here, we present an object-based matrix representation that achieves a very low memory footprint by computing matrix elements on demand instead of explicitly storing them. In doing so, memory required for a dense connectome is reduced to the amount needed to store the underlying time series data. Based on theoretical considerations and benchmarks, different matrix object implementations and additional programs (based on available Matlab functions and Matlab-based third-party software) are compared with regard to their computational efficiency. The matrix implementation based on on-demand computations has very low memory requirements, thus enabling analyses that would be otherwise infeasible to conduct due to insufficient memory. An open source software package containing the created programs is available for download. PMID:27965565

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks.

PubMed

Laniau, Julie; Frioux, Clémence; Nicolas, Jacques; Baroukh, Caroline; Cortes, Maria-Paz; Got, Jeanne; Trottier, Camille; Eveillard, Damien; Siegel, Anne

2017-01-01

The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests , which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype.

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks

PubMed Central

Frioux, Clémence; Nicolas, Jacques; Baroukh, Caroline; Cortes, Maria-Paz; Got, Jeanne; Trottier, Camille; Eveillard, Damien

2017-01-01

Background The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. Results We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. Conclusion The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype. PMID:29038751

Estimation of Psychophysical Thresholds Based on Neural Network Analysis of DPOAE Input/Output Functions

NASA Astrophysics Data System (ADS)

Naghibolhosseini, Maryam; Long, Glenis

2011-11-01

The distortion product otoacoustic emission (DPOAE) input/output (I/O) function may provide a potential tool for evaluating cochlear compression. Hearing loss causes an increase in the level of the sound that is just audible for the person, which affects the cochlea compression and thus the dynamic range of hearing. Although the slope of the I/O function is highly variable when the total DPOAE is used, separating the nonlinear-generator component from the reflection component reduces this variability. We separated the two components using least squares fit (LSF) analysis of logarithmic sweeping tones, and confirmed that the separated generator component provides more consistent I/O functions than the total DPOAE. In this paper we estimated the slope of the I/O functions of the generator components at different sound levels using LSF analysis. An artificial neural network (ANN) was used to estimate psychophysical thresholds using the estimated slopes of the I/O functions. DPOAE I/O functions determined in this way may help to estimate hearing thresholds and cochlear health.

Mixed kernel function support vector regression for global sensitivity analysis

NASA Astrophysics Data System (ADS)

Cheng, Kai; Lu, Zhenzhou; Wei, Yuhao; Shi, Yan; Zhou, Yicheng

2017-11-01

Global sensitivity analysis (GSA) plays an important role in exploring the respective effects of input variables on an assigned output response. Amongst the wide sensitivity analyses in literature, the Sobol indices have attracted much attention since they can provide accurate information for most models. In this paper, a mixed kernel function (MKF) based support vector regression (SVR) model is employed to evaluate the Sobol indices at low computational cost. By the proposed derivation, the estimation of the Sobol indices can be obtained by post-processing the coefficients of the SVR meta-model. The MKF is constituted by the orthogonal polynomials kernel function and Gaussian radial basis kernel function, thus the MKF possesses both the global characteristic advantage of the polynomials kernel function and the local characteristic advantage of the Gaussian radial basis kernel function. The proposed approach is suitable for high-dimensional and non-linear problems. Performance of the proposed approach is validated by various analytical functions and compared with the popular polynomial chaos expansion (PCE). Results demonstrate that the proposed approach is an efficient method for global sensitivity analysis.

Assessment of parametric uncertainty for groundwater reactive transport modeling,

USGS Publications Warehouse

Shi, Xiaoqing; Ye, Ming; Curtis, Gary P.; Miller, Geoffery L.; Meyer, Philip D.; Kohler, Matthias; Yabusaki, Steve; Wu, Jichun

2014-01-01

The validity of using Gaussian assumptions for model residuals in uncertainty quantification of a groundwater reactive transport model was evaluated in this study. Least squares regression methods explicitly assume Gaussian residuals, and the assumption leads to Gaussian likelihood functions, model parameters, and model predictions. While the Bayesian methods do not explicitly require the Gaussian assumption, Gaussian residuals are widely used. This paper shows that the residuals of the reactive transport model are non-Gaussian, heteroscedastic, and correlated in time; characterizing them requires using a generalized likelihood function such as the formal generalized likelihood function developed by Schoups and Vrugt (2010). For the surface complexation model considered in this study for simulating uranium reactive transport in groundwater, parametric uncertainty is quantified using the least squares regression methods and Bayesian methods with both Gaussian and formal generalized likelihood functions. While the least squares methods and Bayesian methods with Gaussian likelihood function produce similar Gaussian parameter distributions, the parameter distributions of Bayesian uncertainty quantification using the formal generalized likelihood function are non-Gaussian. In addition, predictive performance of formal generalized likelihood function is superior to that of least squares regression and Bayesian methods with Gaussian likelihood function. The Bayesian uncertainty quantification is conducted using the differential evolution adaptive metropolis (DREAM(zs)) algorithm; as a Markov chain Monte Carlo (MCMC) method, it is a robust tool for quantifying uncertainty in groundwater reactive transport models. For the surface complexation model, the regression-based local sensitivity analysis and Morris- and DREAM(ZS)-based global sensitivity analysis yield almost identical ranking of parameter importance. The uncertainty analysis may help select appropriate likelihood functions, improve model calibration, and reduce predictive uncertainty in other groundwater reactive transport and environmental modeling.

Dynamics of functional failures and recovery in complex road networks

NASA Astrophysics Data System (ADS)

Zhan, Xianyuan; Ukkusuri, Satish V.; Rao, P. Suresh C.

2017-11-01

We propose a new framework for modeling the evolution of functional failures and recoveries in complex networks, with traffic congestion on road networks as the case study. Differently from conventional approaches, we transform the evolution of functional states into an equivalent dynamic structural process: dual-vertex splitting and coalescing embedded within the original network structure. The proposed model successfully explains traffic congestion and recovery patterns at the city scale based on high-resolution data from two megacities. Numerical analysis shows that certain network structural attributes can amplify or suppress cascading functional failures. Our approach represents a new general framework to model functional failures and recoveries in flow-based networks and allows understanding of the interplay between structure and function for flow-induced failure propagation and recovery.

The Social Action Perspective: Attachments to Work and Productivity in the Research Function.

ERIC Educational Resources Information Center

Rebne, Douglas

1989-01-01

Examines faculty motivation and quantitative performance in research. Factor analysis discloses three bases of employment motivation: moral, calculative, and alienative. Regression analysis indicates that moral and alienative attachments contribute to explaining research productivity. (Author/TE)

Solid Waste Program technical baseline description

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carlson, A.B.

1994-07-01

The system engineering approach has been taken to describe the technical baseline under which the Solid Waste Program is currently operating. The document contains a mission analysis, function analysis, system definition, documentation requirements, facility and project bases, and uncertainties facing the program.

Dynamic mechanical analysis and organization/storage of data for polymetric materials

NASA Technical Reports Server (NTRS)

Rosenberg, M.; Buckley, W.

1982-01-01

Dynamic mechanical analysis was performed on a variety of temperature resistant polymers and composite resin matrices. Data on glass transition temperatures and degree of cure attained were derived. In addition a laboratory based computer system was installed and data base set up to allow entry of composite data. The laboratory CPU termed TYCHO is based on a DEC PDP 11/44 CPU with a Datatrieve relational data base. The function of TYCHO is integration of chemical laboratory analytical instrumentation and storage of chemical structures for modeling of new polymeric structures and compounds

Resting State Network Topology of the Ferret Brain

PubMed Central

Zhou, Zhe Charles; Salzwedel, Andrew P.; Radtke-Schuller, Susanne; Li, Yuhui; Sellers, Kristin K.; Gilmore, John H.; Shih, Yen-Yu Ian; Fröhlich, Flavio; Gao, Wei

2016-01-01

Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4 tesla MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function. PMID:27596024

Biosignals Analysis for Kidney Function Effect Analysis of Fennel Aromatherapy

PubMed Central

Kim, Bong-Hyun; Cho, Dong-Uk; Seo, Ssang-Hee

2015-01-01

Human effort in order to enjoy a healthy life is diverse. IT technology to these analyzes, the results of development efforts, it has been applied. Therefore, I use the care and maintenance diagnostic health management and prevention than treatment. In particular, the aromatherapy treatment easy to use without the side effects there is no irritation, are widely used in modern society. In this paper, we measured the aroma effect by applying a biosignal analysis techniques; an experiment was performed to analyze. In particular, we design methods and processes of research based on the theory aroma that affect renal function. Therefore, in this paper, measuring the biosignals and after fennel aromatherapy treatment prior to the enforcement of the mutual comparison, through the analysis, studies were carried out to analyze the effect of fennel aromatherapy therapy on kidney function. PMID:25977696

Image-derived input function with factor analysis and a-priori information.

PubMed

Simončič, Urban; Zanotti-Fregonara, Paolo

2015-02-01

Quantitative PET studies often require the cumbersome and invasive procedure of arterial cannulation to measure the input function. This study sought to minimize the number of necessary blood samples by developing a factor-analysis-based image-derived input function (IDIF) methodology for dynamic PET brain studies. IDIF estimation was performed as follows: (a) carotid and background regions were segmented manually on an early PET time frame; (b) blood-weighted and tissue-weighted time-activity curves (TACs) were extracted with factor analysis; (c) factor analysis results were denoised and scaled using the voxels with the highest blood signal; (d) using population data and one blood sample at 40 min, whole-blood TAC was estimated from postprocessed factor analysis results; and (e) the parent concentration was finally estimated by correcting the whole-blood curve with measured radiometabolite concentrations. The methodology was tested using data from 10 healthy individuals imaged with [(11)C](R)-rolipram. The accuracy of IDIFs was assessed against full arterial sampling by comparing the area under the curve of the input functions and by calculating the total distribution volume (VT). The shape of the image-derived whole-blood TAC matched the reference arterial curves well, and the whole-blood area under the curves were accurately estimated (mean error 1.0±4.3%). The relative Logan-V(T) error was -4.1±6.4%. Compartmental modeling and spectral analysis gave less accurate V(T) results compared with Logan. A factor-analysis-based IDIF for [(11)C](R)-rolipram brain PET studies that relies on a single blood sample and population data can be used for accurate quantification of Logan-V(T) values.

A distributed microcomputer-controlled system for data acquisition and power spectral analysis of EEG.

PubMed

Vo, T D; Dwyer, G; Szeto, H H

1986-04-01

A relatively powerful and inexpensive microcomputer-based system for the spectral analysis of the EEG is presented. High resolution and speed is achieved with the use of recently available large-scale integrated circuit technology with enhanced functionality (INTEL Math co-processors 8087) which can perform transcendental functions rapidly. The versatility of the system is achieved with a hardware organization that has distributed data acquisition capability performed by the use of a microprocessor-based analog to digital converter with large resident memory (Cyborg ISAAC-2000). Compiled BASIC programs and assembly language subroutines perform on-line or off-line the fast Fourier transform and spectral analysis of the EEG which is stored as soft as well as hard copy. Some results obtained from test application of the entire system in animal studies are presented.

Survival analysis with functional covariates for partial follow-up studies.

PubMed

Fang, Hong-Bin; Wu, Tong Tong; Rapoport, Aaron P; Tan, Ming

2016-12-01

Predictive or prognostic analysis plays an increasingly important role in the era of personalized medicine to identify subsets of patients whom the treatment may benefit the most. Although various time-dependent covariate models are available, such models require that covariates be followed in the whole follow-up period. This article studies a new class of functional survival models where the covariates are only monitored in a time interval that is shorter than the whole follow-up period. This paper is motivated by the analysis of a longitudinal study on advanced myeloma patients who received stem cell transplants and T cell infusions after the transplants. The absolute lymphocyte cell counts were collected serially during hospitalization. Those patients are still followed up if they are alive after hospitalization, while their absolute lymphocyte cell counts cannot be measured after that. Another complication is that absolute lymphocyte cell counts are sparsely and irregularly measured. The conventional method using Cox model with time-varying covariates is not applicable because of the different lengths of observation periods. Analysis based on each single observation obviously underutilizes available information and, more seriously, may yield misleading results. This so-called partial follow-up study design represents increasingly common predictive modeling problem where we have serial multiple biomarkers up to a certain time point, which is shorter than the total length of follow-up. We therefore propose a solution to the partial follow-up design. The new method combines functional principal components analysis and survival analysis with selection of those functional covariates. It also has the advantage of handling sparse and irregularly measured longitudinal observations of covariates and measurement errors. Our analysis based on functional principal components reveals that it is the patterns of the trajectories of absolute lymphocyte cell counts, instead of the actual counts, that affect patient's disease-free survival time. © The Author(s) 2014.

A genome-wide analysis of putative functional and exonic variation associated with extremely high intelligence

PubMed Central

Spain, S L; Pedroso, I; Kadeva, N; Miller, M B; Iacono, W G; McGue, M; Stergiakouli, E; Smith, G D; Putallaz, M; Lubinski, D; Meaburn, E L; Plomin, R; Simpson, M A

2016-01-01

Although individual differences in intelligence (general cognitive ability) are highly heritable, molecular genetic analyses to date have had limited success in identifying specific loci responsible for its heritability. This study is the first to investigate exome variation in individuals of extremely high intelligence. Under the quantitative genetic model, sampling from the high extreme of the distribution should provide increased power to detect associations. We therefore performed a case–control association analysis with 1409 individuals drawn from the top 0.0003 (IQ >170) of the population distribution of intelligence and 3253 unselected population-based controls. Our analysis focused on putative functional exonic variants assayed on the Illumina HumanExome BeadChip. We did not observe any individual protein-altering variants that are reproducibly associated with extremely high intelligence and within the entire distribution of intelligence. Moreover, no significant associations were found for multiple rare alleles within individual genes. However, analyses using genome-wide similarity between unrelated individuals (genome-wide complex trait analysis) indicate that the genotyped functional protein-altering variation yields a heritability estimate of 17.4% (s.e. 1.7%) based on a liability model. In addition, investigation of nominally significant associations revealed fewer rare alleles associated with extremely high intelligence than would be expected under the null hypothesis. This observation is consistent with the hypothesis that rare functional alleles are more frequently detrimental than beneficial to intelligence. PMID:26239293

A genome-wide analysis of putative functional and exonic variation associated with extremely high intelligence.

PubMed

Spain, S L; Pedroso, I; Kadeva, N; Miller, M B; Iacono, W G; McGue, M; Stergiakouli, E; Davey Smith, G; Putallaz, M; Lubinski, D; Meaburn, E L; Plomin, R; Simpson, M A

2016-08-01

Although individual differences in intelligence (general cognitive ability) are highly heritable, molecular genetic analyses to date have had limited success in identifying specific loci responsible for its heritability. This study is the first to investigate exome variation in individuals of extremely high intelligence. Under the quantitative genetic model, sampling from the high extreme of the distribution should provide increased power to detect associations. We therefore performed a case-control association analysis with 1409 individuals drawn from the top 0.0003 (IQ >170) of the population distribution of intelligence and 3253 unselected population-based controls. Our analysis focused on putative functional exonic variants assayed on the Illumina HumanExome BeadChip. We did not observe any individual protein-altering variants that are reproducibly associated with extremely high intelligence and within the entire distribution of intelligence. Moreover, no significant associations were found for multiple rare alleles within individual genes. However, analyses using genome-wide similarity between unrelated individuals (genome-wide complex trait analysis) indicate that the genotyped functional protein-altering variation yields a heritability estimate of 17.4% (s.e. 1.7%) based on a liability model. In addition, investigation of nominally significant associations revealed fewer rare alleles associated with extremely high intelligence than would be expected under the null hypothesis. This observation is consistent with the hypothesis that rare functional alleles are more frequently detrimental than beneficial to intelligence.

Accuracy analysis of automodel solutions for Lévy flight-based transport: from resonance radiative transfer to a simple general model

NASA Astrophysics Data System (ADS)

Kukushkin, A. B.; Sdvizhenskii, P. A.

2017-12-01

The results of accuracy analysis of automodel solutions for Lévy flight-based transport on a uniform background are presented. These approximate solutions have been obtained for Green’s function of the following equations: the non-stationary Biberman-Holstein equation for three-dimensional (3D) radiative transfer in plasma and gases, for various (Doppler, Lorentz, Voigt and Holtsmark) spectral line shapes, and the 1D transport equation with a simple longtailed step-length probability distribution function with various power-law exponents. The results suggest the possibility of substantial extension of the developed method of automodel solution to other fields far beyond physics.

Dielectric spectroscopy of PMMA-LiClO4 based polymer electrolyte plasticized with ethylene carbonate EC

NASA Astrophysics Data System (ADS)

Pal, P.; Ghosh, A.

2018-04-01

Dielectric spectroscopy covering the frequency range 0.01 Hz - 2 MHz for PMMA-LiClO4 based polymer electrolyte embedded with different concentration of ethylene carbonate (x = 0, 20 and 40 wt%) has been analyzed using Havrilliak-Negami formalism. The reciprocal temperature dependence of inverse relaxation time obtained from the analysis of dielectric spectra follows Vogel-Tammann-Fulcher behaviour. The shape parameters obtained from this analysis change with ethylene carbonate concentrations. From the fits of the experimental result using Kohlrausch-Williams-Watts function. We have obtained stretched exponent β which indicates that the relaxation is highly non-exponential. The decay function obtained from electric modulus data is highly asymmetric.

Solution identification and quantitative analysis of fiber-capacitive drop analyzer based on multivariate statistical methods

NASA Astrophysics Data System (ADS)

Chen, Zhe; Qiu, Zurong; Huo, Xinming; Fan, Yuming; Li, Xinghua

2017-03-01

A fiber-capacitive drop analyzer is an instrument which monitors a growing droplet to produce a capacitive opto-tensiotrace (COT). Each COT is an integration of fiber light intensity signals and capacitance signals and can reflect the unique physicochemical property of a liquid. In this study, we propose a solution analytical and concentration quantitative method based on multivariate statistical methods. Eight characteristic values are extracted from each COT. A series of COT characteristic values of training solutions at different concentrations compose a data library of this kind of solution. A two-stage linear discriminant analysis is applied to analyze different solution libraries and establish discriminant functions. Test solutions can be discriminated by these functions. After determining the variety of test solutions, Spearman correlation test and principal components analysis are used to filter and reduce dimensions of eight characteristic values, producing a new representative parameter. A cubic spline interpolation function is built between the parameters and concentrations, based on which we can calculate the concentration of the test solution. Methanol, ethanol, n-propanol, and saline solutions are taken as experimental subjects in this paper. For each solution, nine or ten different concentrations are chosen to be the standard library, and the other two concentrations compose the test group. By using the methods mentioned above, all eight test solutions are correctly identified and the average relative error of quantitative analysis is 1.11%. The method proposed is feasible which enlarges the applicable scope of recognizing liquids based on the COT and improves the concentration quantitative precision, as well.

Grey Matter Alterations Co-Localize with Functional Abnormalities in Developmental Dyslexia: An ALE Meta-Analysis

PubMed Central

Linkersdörfer, Janosch; Lonnemann, Jan; Lindberg, Sven; Hasselhorn, Marcus; Fiebach, Christian J.

2012-01-01

The neural correlates of developmental dyslexia have been investigated intensively over the last two decades and reliable evidence for a dysfunction of left-hemispheric reading systems in dyslexic readers has been found in functional neuroimaging studies. In addition, structural imaging studies using voxel-based morphometry (VBM) demonstrated grey matter reductions in dyslexics in several brain regions. To objectively assess the consistency of these findings, we performed activation likelihood estimation (ALE) meta-analysis on nine published VBM studies reporting 62 foci of grey matter reduction in dyslexic readers. We found six significant clusters of convergence in bilateral temporo-parietal and left occipito-temporal cortical regions and in the cerebellum bilaterally. To identify possible overlaps between structural and functional deviations in dyslexic readers, we conducted additional ALE meta-analyses of imaging studies reporting functional underactivations (125 foci from 24 studies) or overactivations (95 foci from 11 studies ) in dyslexics. Subsequent conjunction analyses revealed overlaps between the results of the VBM meta-analysis and the meta-analysis of functional underactivations in the fusiform and supramarginal gyri of the left hemisphere. An overlap between VBM results and the meta-analysis of functional overactivations was found in the left cerebellum. The results of our study provide evidence for consistent grey matter variations bilaterally in the dyslexic brain and substantial overlap of these structural variations with functional abnormalities in left hemispheric regions. PMID:22916214

Students' Perceptions of Study Modes

ERIC Educational Resources Information Center

Hagel, Pauline; Shaw, Robin N.

2006-01-01

This paper reports on a survey of how Australian undergraduate students perceive the benefits of broad study modes: face-to-face classes, web-based study, and print-based study. Two benefit types were identified through factor analysis: engagement and functionality. Respondents rated face-to-face classes highest on engagement and print-based study…

Functional analysis and intervention for perseverative verbal behaviour of an older adult with traumatic brain injury.

PubMed

Quearry, Amy Garcia; Lundervold, Duane A

2016-01-01

A functional analysis of behaviour was conducted to determine the controlling variables related to the perseverative verbal behaviour (PBV) of a 60-year-old female with a long-standing traumatic brain injury receiving educational assistance. Functional analyses (FA) of antecedent and consequent conditions related to PCB were conducted to determine controlling influence of: (a) content of verbal interaction and, (b) social reinforcement. After isolating the controlling variables, the functioned-based intervention was implemented in 60 minute tutoring sessions. A reversal condition was used to demonstrate experimental control of the behavior during tutoring sessions. PVB which occurred in the context of tutoring for an undergraduate course significantly interfered with the delivery of instruction. Multiple replications of the functional relation between social reinforcement and PVB duration was demonstrated using an A-B-A-B reversal design during functional analysis and tutoring conditions. PVB markedly declined, but did not extinguish over the course of weekly tutoring (extinction) sessions, most likely due to 'bootleg reinforcement' occurring in other situations. Results indicate that perseverative verbal behaviour following closed head injury may be strongly influenced by the social contingencies operating in various contexts and is amenable to applied behaviour analysis interventions.

Time-domain representation of frequency-dependent foundation impedance functions

USGS Publications Warehouse

Safak, E.

2006-01-01

Foundation impedance functions provide a simple means to account for soil-structure interaction (SSI) when studying seismic response of structures. Impedance functions represent the dynamic stiffness of the soil media surrounding the foundation. The fact that impedance functions are frequency dependent makes it difficult to incorporate SSI in standard time-history analysis software. This paper introduces a simple method to convert frequency-dependent impedance functions into time-domain filters. The method is based on the least-squares approximation of impedance functions by ratios of two complex polynomials. Such ratios are equivalent, in the time-domain, to discrete-time recursive filters, which are simple finite-difference equations giving the relationship between foundation forces and displacements. These filters can easily be incorporated into standard time-history analysis programs. Three examples are presented to show the applications of the method.

Biosensors for functional food safety and analysis.

PubMed

Lavecchia, Teresa; Tibuzzi, Arianna; Giardi, Maria Teresa

2010-01-01

The importance of safety and functionality analysis of foodstuffs and raw materials is supported by national legislations and European Union (EU) directives concerning not only the amount of residues of pollutants and pathogens but also the activity and content of food additives and the health claims stated on their labels. In addition, consumers' awareness of the impact of functional foods' on their well-being and their desire for daily healthcare without the intake pharmaceuticals has immensely in recent years. Within this picture, the availability of fast, reliable, low cost control systems to measure the content and the quality of food additives and nutrients with health claims becomes mandatory, to be used by producers, consumers and the governmental bodies in charge of the legal supervision of such matters. This review aims at describing the most important methods and tools used for food analysis, starting with the classical methods (e.g., gas-chromatography GC, high performance liquid chromatography HPLC) and moving to the use of biosensors-novel biological material-based equipments. Four types of bio-sensors, among others, the novel photosynthetic proteins-based devices which are more promising and common in food analysis applications, are reviewed. A particular highlight on biosensors for the emerging market of functional foods is given and the most widely applied functional components are reviewed with a comprehensive analysis of papers published in the last three years; this report discusses recent trends for sensitive, fast, repeatable and cheap measurements, focused on the detection of vitamins, folate (folic acid), zinc (Zn), iron (Fe), calcium (Ca), fatty acids (in particular Omega 3), phytosterols and phytochemicals. A final market overview emphasizes some practical aspects ofbiosensor applications.

Statistical inference of dynamic resting-state functional connectivity using hierarchical observation modeling.

PubMed

Sojoudi, Alireza; Goodyear, Bradley G

2016-12-01

Spontaneous fluctuations of blood-oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI) signals are highly synchronous between brain regions that serve similar functions. This provides a means to investigate functional networks; however, most analysis techniques assume functional connections are constant over time. This may be problematic in the case of neurological disease, where functional connections may be highly variable. Recently, several methods have been proposed to determine moment-to-moment changes in the strength of functional connections over an imaging session (so called dynamic connectivity). Here a novel analysis framework based on a hierarchical observation modeling approach was proposed, to permit statistical inference of the presence of dynamic connectivity. A two-level linear model composed of overlapping sliding windows of fMRI signals, incorporating the fact that overlapping windows are not independent was described. To test this approach, datasets were synthesized whereby functional connectivity was either constant (significant or insignificant) or modulated by an external input. The method successfully determines the statistical significance of a functional connection in phase with the modulation, and it exhibits greater sensitivity and specificity in detecting regions with variable connectivity, when compared with sliding-window correlation analysis. For real data, this technique possesses greater reproducibility and provides a more discriminative estimate of dynamic connectivity than sliding-window correlation analysis. Hum Brain Mapp 37:4566-4580, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Longitudinal structure function from logarithmic slopes of F2 at low x

NASA Astrophysics Data System (ADS)

Boroun, G. R.

2018-01-01

Using Laplace transform techniques, I calculate the longitudinal structure function FL(x ,Q2) from the scaling violations of the proton structure function F2(x ,Q2) and make a critical study of this relationship between the structure functions at leading order (LO) up to next-to-next-to leading order (NNLO) analysis at small x . Furthermore, I consider heavy quark contributions to the relation between the structure functions, which leads to compact formula for Nf=3 +Heavy . The nonlinear corrections to the longitudinal structure function at LO up to NNLO analysis are shown in the Nf=4 (light quark flavor) based on the nonlinear corrections at R =2 and R =4 GeV-1 . The results are compared with experimental data of the longitudinal proton structure function FL in the range of 6.5 ≤Q2≤800 GeV2 .

Usalpharma: A Cloud-Based Architecture to Support Quality Assurance Training Processes in Health Area Using Virtual Worlds

PubMed Central

García-Peñalvo, Francisco J.; Pérez-Blanco, Jonás Samuel; Martín-Suárez, Ana

2014-01-01

This paper discusses how cloud-based architectures can extend and enhance the functionality of the training environments based on virtual worlds and how, from this cloud perspective, we can provide support to analysis of training processes in the area of health, specifically in the field of training processes in quality assurance for pharmaceutical laboratories, presenting a tool for data retrieval and analysis that allows facing the knowledge discovery in the happenings inside the virtual worlds. PMID:24778593

Preface Sections in English and Arabic Linguistics Books: A Rhetorico-Cultural Analysis

ERIC Educational Resources Information Center

Al-Zubaidi, Nassier A. G.; Jasim, Tahani Awad

2016-01-01

The present paper is a genre analysis of linguistics books prefaces in English and Arabic. Following Swales' (1990) genre framework, this study is a small scale-based generic analysis of 80 preface texts, equally divided into 40 texts from English and Arabic. The corpus analysis revealed that to perform its communicative function, the genre of the…

COMAN: a web server for comprehensive metatranscriptomics analysis.

PubMed

Ni, Yueqiong; Li, Jun; Panagiotou, Gianni

2016-08-11

Microbiota-oriented studies based on metagenomic or metatranscriptomic sequencing have revolutionised our understanding on microbial ecology and the roles of both clinical and environmental microbes. The analysis of massive metatranscriptomic data requires extensive computational resources, a collection of bioinformatics tools and expertise in programming. We developed COMAN (Comprehensive Metatranscriptomics Analysis), a web-based tool dedicated to automatically and comprehensively analysing metatranscriptomic data. COMAN pipeline includes quality control of raw reads, removal of reads derived from non-coding RNA, followed by functional annotation, comparative statistical analysis, pathway enrichment analysis, co-expression network analysis and high-quality visualisation. The essential data generated by COMAN are also provided in tabular format for additional analysis and integration with other software. The web server has an easy-to-use interface and detailed instructions, and is freely available at http://sbb.hku.hk/COMAN/ CONCLUSIONS: COMAN is an integrated web server dedicated to comprehensive functional analysis of metatranscriptomic data, translating massive amount of reads to data tables and high-standard figures. It is expected to facilitate the researchers with less expertise in bioinformatics in answering microbiota-related biological questions and to increase the accessibility and interpretation of microbiota RNA-Seq data.

Yager’s ranking method for solving the trapezoidal fuzzy number linear programming

NASA Astrophysics Data System (ADS)

Karyati; Wutsqa, D. U.; Insani, N.

2018-03-01

In the previous research, the authors have studied the fuzzy simplex method for trapezoidal fuzzy number linear programming based on the Maleki’s ranking function. We have found some theories related to the term conditions for the optimum solution of fuzzy simplex method, the fuzzy Big-M method, the fuzzy two-phase method, and the sensitivity analysis. In this research, we study about the fuzzy simplex method based on the other ranking function. It is called Yager's ranking function. In this case, we investigate the optimum term conditions. Based on the result of research, it is found that Yager’s ranking function is not like Maleki’s ranking function. Using the Yager’s function, the simplex method cannot work as well as when using the Maleki’s function. By using the Yager’s function, the value of the subtraction of two equal fuzzy numbers is not equal to zero. This condition makes the optimum table of the fuzzy simplex table is undetected. As a result, the simplified fuzzy simplex table becomes stopped and does not reach the optimum solution.

Uncertainty Analysis via Failure Domain Characterization: Unrestricted Requirement Functions

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2011-01-01

This paper proposes an uncertainty analysis framework based on the characterization of the uncertain parameter space. This characterization enables the identification of worst-case uncertainty combinations and the approximation of the failure and safe domains with a high level of accuracy. Because these approximations are comprised of subsets of readily computable probability, they enable the calculation of arbitrarily tight upper and lower bounds to the failure probability. The methods developed herein, which are based on nonlinear constrained optimization, are applicable to requirement functions whose functional dependency on the uncertainty is arbitrary and whose explicit form may even be unknown. Some of the most prominent features of the methodology are the substantial desensitization of the calculations from the assumed uncertainty model (i.e., the probability distribution describing the uncertainty) as well as the accommodation for changes in such a model with a practically insignificant amount of computational effort.

Density Functional Theory (DFT) Study of Molecularly Imprinted Polymer (MIP) Methacrylic Acid (MAA) with D-Glucose

NASA Astrophysics Data System (ADS)

Wungu, T. D. K.; Marsha, S. E.; Widayani; Suprijadi

2017-07-01

In order to find an alternative biosensor material which enables to detect the glucose level, therefore in this study, the interaction between Methacrylic Acid (MAA) based Molecularly Imprinted Polymer (MIP) with D-Glucose is investigated using the Density Functional Theory (DFT). The aim of this study is to determine whether a molecule of the MAA can be functioned as a bio-sensing of glucose. In this calculation, the Gaussian 09 with B3LYP and 631+G(d) basis sets is used to calculate all electronic properties. It is found that the interaction between a molecule of MAA and a molecule of D-Glucose was observed through the shortened distance between the two molecules. The binding energy of MAA/D-glucose and the Mulliken population analysis are investigated for checking possible interaction. From analysis, the MAA based MIP can be used as a bio-sensing material.

Functionalizing Microporous Membranes for Protein Purification and Protein Digestion

NASA Astrophysics Data System (ADS)

Dong, Jinlan; Bruening, Merlin L.

2015-07-01

This review examines advances in the functionalization of microporous membranes for protein purification and the development of protease-containing membranes for controlled protein digestion prior to mass spectrometry analysis. Recent studies confirm that membranes are superior to bead-based columns for rapid protein capture, presumably because convective mass transport in membrane pores rapidly brings proteins to binding sites. Modification of porous membranes with functional polymeric films or TiO2 nanoparticles yields materials that selectively capture species ranging from phosphopeptides to His-tagged proteins, and protein-binding capacities often exceed those of commercial beads. Thin membranes also provide a convenient framework for creating enzyme-containing reactors that afford control over residence times. With millisecond residence times, reactors with immobilized proteases limit protein digestion to increase sequence coverage in mass spectrometry analysis and facilitate elucidation of protein structures. This review emphasizes the advantages of membrane-based techniques and concludes with some challenges for their practical application.

Functionalizing Microporous Membranes for Protein Purification and Protein Digestion.

PubMed

Dong, Jinlan; Bruening, Merlin L

2015-01-01

This review examines advances in the functionalization of microporous membranes for protein purification and the development of protease-containing membranes for controlled protein digestion prior to mass spectrometry analysis. Recent studies confirm that membranes are superior to bead-based columns for rapid protein capture, presumably because convective mass transport in membrane pores rapidly brings proteins to binding sites. Modification of porous membranes with functional polymeric films or TiO₂ nanoparticles yields materials that selectively capture species ranging from phosphopeptides to His-tagged proteins, and protein-binding capacities often exceed those of commercial beads. Thin membranes also provide a convenient framework for creating enzyme-containing reactors that afford control over residence times. With millisecond residence times, reactors with immobilized proteases limit protein digestion to increase sequence coverage in mass spectrometry analysis and facilitate elucidation of protein structures. This review emphasizes the advantages of membrane-based techniques and concludes with some challenges for their practical application.

Functional clustering of time series gene expression data by Granger causality

PubMed Central

2012-01-01

Background A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes. Results In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence. Conclusions This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them. PMID:23107425

Meta-Analysis of Structured Triglyceride versus Physical Mixture Medium- and Long-Chain Triglycerides for PN in Liver Resection Patients.

PubMed

Zhao, Yajie; Wang, Chengfeng

2017-01-01

The use of total parenteral nutrition can affect liver function, causing a series of problems such as cholestasis. The aim of this meta-analysis was to compare structured triglyceride- (STG-) based lipid emulsions with physical medium-chain triglyceride (MCT)/long-chain triglyceride (LCT) mixtures in patients who had undergone liver surgery to identify any differences between these two types of parenteral nutrition. We searched the databases of PubMed, the Cochrane Library, Web of Science, EMBASE, and Chinese Biomedicine Database from January 2007 to March 2017 and included studies that compared STG-based lipid emulsions with physical MCT/LCT mixtures for surgical patients with liver disease. The STG was more beneficial than physical MCT/LCT on recovery of liver function and immune function. Therefore, STGs may represent a promising alternative to other types of lipid emulsions for hepatic surgery patients.

Exploring representations of protein structure for automated remote homology detection and mapping of protein structure space

PubMed Central

2014-01-01

Background Due to rapid sequencing of genomes, there are now millions of deposited protein sequences with no known function. Fast sequence-based comparisons allow detecting close homologs for a protein of interest to transfer functional information from the homologs to the given protein. Sequence-based comparison cannot detect remote homologs, in which evolution has adjusted the sequence while largely preserving structure. Structure-based comparisons can detect remote homologs but most methods for doing so are too expensive to apply at a large scale over structural databases of proteins. Recently, fragment-based structural representations have been proposed that allow fast detection of remote homologs with reasonable accuracy. These representations have also been used to obtain linearly-reducible maps of protein structure space. It has been shown, as additionally supported from analysis in this paper that such maps preserve functional co-localization of the protein structure space. Methods Inspired by a recent application of the Latent Dirichlet Allocation (LDA) model for conducting structural comparisons of proteins, we propose higher-order LDA-obtained topic-based representations of protein structures to provide an alternative route for remote homology detection and organization of the protein structure space in few dimensions. Various techniques based on natural language processing are proposed and employed to aid the analysis of topics in the protein structure domain. Results We show that a topic-based representation is just as effective as a fragment-based one at automated detection of remote homologs and organization of protein structure space. We conduct a detailed analysis of the information content in the topic-based representation, showing that topics have semantic meaning. The fragment-based and topic-based representations are also shown to allow prediction of superfamily membership. Conclusions This work opens exciting venues in designing novel representations to extract information about protein structures, as well as organizing and mining protein structure space with mature text mining tools. PMID:25080993

Computational Selection of Transcriptomics Experiments Improves Guilt-by-Association Analyses

PubMed Central

Bhat, Prajwal; Yang, Haixuan; Bögre, László; Devoto, Alessandra; Paccanaro, Alberto

2012-01-01

The Guilt-by-Association (GBA) principle, according to which genes with similar expression profiles are functionally associated, is widely applied for functional analyses using large heterogeneous collections of transcriptomics data. However, the use of such large collections could hamper GBA functional analysis for genes whose expression is condition specific. In these cases a smaller set of condition related experiments should instead be used, but identifying such functionally relevant experiments from large collections based on literature knowledge alone is an impractical task. We begin this paper by analyzing, both from a mathematical and a biological point of view, why only condition specific experiments should be used in GBA functional analysis. We are able to show that this phenomenon is independent of the functional categorization scheme and of the organisms being analyzed. We then present a semi-supervised algorithm that can select functionally relevant experiments from large collections of transcriptomics experiments. Our algorithm is able to select experiments relevant to a given GO term, MIPS FunCat term or even KEGG pathways. We extensively test our algorithm on large dataset collections for yeast and Arabidopsis. We demonstrate that: using the selected experiments there is a statistically significant improvement in correlation between genes in the functional category of interest; the selected experiments improve GBA-based gene function prediction; the effectiveness of the selected experiments increases with annotation specificity; our algorithm can be successfully applied to GBA-based pathway reconstruction. Importantly, the set of experiments selected by the algorithm reflects the existing literature knowledge about the experiments. [A MATLAB implementation of the algorithm and all the data used in this paper can be downloaded from the paper website: http://www.paccanarolab.org/papers/CorrGene/]. PMID:22879875

Percolation analysis for cosmic web with discrete points

NASA Astrophysics Data System (ADS)

Zhang, Jiajun; Cheng, Dalong; Chu, Ming-Chung

2018-01-01

Percolation analysis has long been used to quantify the connectivity of the cosmic web. Most of the previous work is based on density fields on grids. By smoothing into fields, we lose information about galaxy properties like shape or luminosity. The lack of mathematical modeling also limits our understanding for the percolation analysis. To overcome these difficulties, we have studied percolation analysis based on discrete points. Using a friends-of-friends (FoF) algorithm, we generate the S -b b relation, between the fractional mass of the largest connected group (S ) and the FoF linking length (b b ). We propose a new model, the probability cloud cluster expansion theory to relate the S -b b relation with correlation functions. We show that the S -b b relation reflects a combination of all orders of correlation functions. Using N-body simulation, we find that the S -b b relation is robust against redshift distortion and incompleteness in observation. From the Bolshoi simulation, with halo abundance matching (HAM), we have generated a mock galaxy catalog. Good matching of the projected two-point correlation function with observation is confirmed. However, comparing the mock catalog with the latest galaxy catalog from Sloan Digital Sky Survey (SDSS) Data Release (DR)12, we have found significant differences in their S -b b relations. This indicates that the mock galaxy catalog cannot accurately retain higher-order correlation functions than the two-point correlation function, which reveals the limit of the HAM method. As a new measurement, the S -b b relation is applicable to a wide range of data types, fast to compute, and robust against redshift distortion and incompleteness and contains information of all orders of correlation functions.

Analysis of radiometric signal in sedimentating suspension flow in open channel

NASA Astrophysics Data System (ADS)

Zych, Marcin; Hanus, Robert; Petryka, Leszek; Świsulski, Dariusz; Doktor, Marek; Mastej, Wojciech

2015-05-01

The article discusses issues related to the estimation of the sedimentating solid particles average flow velocity in an open channel using radiometric methods. Due to the composition of the compound, which formed water and diatomite, received data have a very weak signal to noise ratio. In the process analysis the known determining of the solid phase transportation time delay the classical cross-correlation function is the most reliable method. The use of advanced frequency analysis based on mutual spectral density function and wavelet transform of recorded signals allows a reduction of the noise contribution.

Multivariate Cluster Analysis.

ERIC Educational Resources Information Center

McRae, Douglas J.

Procedures for grouping students into homogeneous subsets have long interested educational researchers. The research reported in this paper is an investigation of a set of objective grouping procedures based on multivariate analysis considerations. Four multivariate functions that might serve as criteria for adequate grouping are given and…

Characterisation of the novel deleterious RAD51C p.Arg312Trp variant and prioritisation criteria for functional analysis of RAD51C missense changes.

PubMed

Gayarre, Javier; Martín-Gimeno, Paloma; Osorio, Ana; Paumard, Beatriz; Barroso, Alicia; Fernández, Victoria; de la Hoya, Miguel; Rojo, Alejandro; Caldés, Trinidad; Palacios, José; Urioste, Miguel; Benítez, Javier; García, María J

2017-09-26

Despite a high prevalence of deleterious missense variants, most studies of RAD51C ovarian cancer susceptibility gene only provide in silico pathogenicity predictions of missense changes. We identified a novel deleterious RAD51C missense variant (p.Arg312Trp) in a high-risk family, and propose a criteria to prioritise RAD51C missense changes qualifying for functional analysis. To evaluate pathogenicity of p.Arg312Trp variant we used sequence homology, loss of heterozygosity (LOH) and segregation analysis, and a comprehensive functional characterisation. To define a functional-analysis prioritisation criteria, we used outputs for the known functionally confirmed deleterious and benign RAD51C missense changes from nine pathogenicity prediction algorithms. The p.Arg312Trp variant failed to correct mitomycin and olaparib hypersensitivity and to complement abnormal RAD51C foci formation according to functional assays, which altogether with LOH and segregation data demonstrated deleteriousness. Prioritisation criteria were based on the number of predictors providing a deleterious output, with a minimum of 5 to qualify for testing and a PredictProtein score greater than 33 to assign high-priority indication. Our study points to a non-negligible number of RAD51C missense variants likely to impair protein function, provides a guideline to prioritise and encourage their selection for functional analysis and anticipates that reference laboratories should have available resources to conduct such assays.

Validation of the Female Sexual Function Index (FSFI) for web-based administration.

PubMed

Crisp, Catrina C; Fellner, Angela N; Pauls, Rachel N

2015-02-01

Web-based questionnaires are becoming increasingly valuable for clinical research. The Female Sexual Function Index (FSFI) is the gold standard for evaluating female sexual function; yet, it has not been validated in this format. We sought to validate the Female Sexual Function Index (FSFI) for web-based administration. Subjects enrolled in a web-based research survey of sexual function from the general population were invited to participate in this validation study. The first 151 respondents were included. Validation participants completed the web-based version of the FSFI followed by a mailed paper-based version. Demographic data were collected for all subjects. Scores were compared using the paired t test and the intraclass correlation coefficient. One hundred fifty-one subjects completed both web- and paper-based versions of the FSFI. Those subjects participating in the validation study did not differ in demographics or FSFI scores from the remaining subjects in the general population study. Total web-based and paper-based FSFI scores were not significantly different (mean 20.31 and 20.29 respectively, p = 0.931). The six domains or subscales of the FSFI were similar when comparing web and paper scores. Finally, intraclass correlation analysis revealed a high degree of correlation between total and subscale scores, r = 0.848-0.943, p < 0.001. Web-based administration of the FSFI is a valid alternative to the paper-based version.

Decreased functional connectivity of insula-based network in young adults with internet gaming disorder.

PubMed

Zhang, Yanzhen; Mei, Wei; Zhang, John X; Wu, Qiulin; Zhang, Wei

2016-09-01

The insula is a region that integrates interoception and drug urges, but little is known about its role in behavioral addiction such as internet addiction. We investigated insula-based functional connectivity in participants with internet gaming disorder (IGD) and healthy controls (HC) using resting-state functional MRI. The right and left insula subregions (posterior, ventroanterior, and dorsoanterior) were used as seed regions in a connectivity analysis. Compared with the HC group, the IGD group showed decreased functional connectivity between left posterior insula and bilateral supplementary motor area and middle cingulated cortex, between right posterior insula and right superior frontal gyrus, and decreased functional integration between insular subregions. The finding of reduced functional connectivity between the interoception and the motor/executive control regions is interpreted to reflect reduced ability to inhibit motor responses to internet gaming or diminished executive control over craving for internet gaming in IGD. The results support the hypothesis that IGD is associated with altered insula-based network, similar to substance addiction such as smoking.

Space-time models based on random fields with local interactions

NASA Astrophysics Data System (ADS)

Hristopulos, Dionissios T.; Tsantili, Ivi C.

2016-08-01

The analysis of space-time data from complex, real-life phenomena requires the use of flexible and physically motivated covariance functions. In most cases, it is not possible to explicitly solve the equations of motion for the fields or the respective covariance functions. In the statistical literature, covariance functions are often based on mathematical constructions. In this paper, we propose deriving space-time covariance functions by solving “effective equations of motion”, which can be used as statistical representations of systems with diffusive behavior. In particular, we propose to formulate space-time covariance functions based on an equilibrium effective Hamiltonian using the linear response theory. The effective space-time dynamics is then generated by a stochastic perturbation around the equilibrium point of the classical field Hamiltonian leading to an associated Langevin equation. We employ a Hamiltonian which extends the classical Gaussian field theory by including a curvature term and leads to a diffusive Langevin equation. Finally, we derive new forms of space-time covariance functions.

An automated multi-scale network-based scheme for detection and location of seismic sources

NASA Astrophysics Data System (ADS)

Poiata, N.; Aden-Antoniow, F.; Satriano, C.; Bernard, P.; Vilotte, J. P.; Obara, K.

2017-12-01

We present a recently developed method - BackTrackBB (Poiata et al. 2016) - allowing to image energy radiation from different seismic sources (e.g., earthquakes, LFEs, tremors) in different tectonic environments using continuous seismic records. The method exploits multi-scale frequency-selective coherence in the wave field, recorded by regional seismic networks or local arrays. The detection and location scheme is based on space-time reconstruction of the seismic sources through an imaging function built from the sum of station-pair time-delay likelihood functions, projected onto theoretical 3D time-delay grids. This imaging function is interpreted as the location likelihood of the seismic source. A signal pre-processing step constructs a multi-band statistical representation of the non stationary signal, i.e. time series, by means of higher-order statistics or energy envelope characteristic functions. Such signal-processing is designed to detect in time signal transients - of different scales and a priori unknown predominant frequency - potentially associated with a variety of sources (e.g., earthquakes, LFE, tremors), and to improve the performance and the robustness of the detection-and-location location step. The initial detection-location, based on a single phase analysis with the P- or S-phase only, can then be improved recursively in a station selection scheme. This scheme - exploiting the 3-component records - makes use of P- and S-phase characteristic functions, extracted after a polarization analysis of the event waveforms, and combines the single phase imaging functions with the S-P differential imaging functions. The performance of the method is demonstrated here in different tectonic environments: (1) analysis of the one year long precursory phase of 2014 Iquique earthquake in Chile; (2) detection and location of tectonic tremor sources and low-frequency earthquakes during the multiple episodes of tectonic tremor activity in southwestern Japan.

A robust functional-data-analysis method for data recovery in multichannel sensor systems.

PubMed

Sun, Jian; Liao, Haitao; Upadhyaya, Belle R

2014-08-01

Multichannel sensor systems are widely used in condition monitoring for effective failure prevention of critical equipment or processes. However, loss of sensor readings due to malfunctions of sensors and/or communication has long been a hurdle to reliable operations of such integrated systems. Moreover, asynchronous data sampling and/or limited data transmission are usually seen in multiple sensor channels. To reliably perform fault diagnosis and prognosis in such operating environments, a data recovery method based on functional principal component analysis (FPCA) can be utilized. However, traditional FPCA methods are not robust to outliers and their capabilities are limited in recovering signals with strongly skewed distributions (i.e., lack of symmetry). This paper provides a robust data-recovery method based on functional data analysis to enhance the reliability of multichannel sensor systems. The method not only considers the possibly skewed distribution of each channel of signal trajectories, but is also capable of recovering missing data for both individual and correlated sensor channels with asynchronous data that may be sparse as well. In particular, grand median functions, rather than classical grand mean functions, are utilized for robust smoothing of sensor signals. Furthermore, the relationship between the functional scores of two correlated signals is modeled using multivariate functional regression to enhance the overall data-recovery capability. An experimental flow-control loop that mimics the operation of coolant-flow loop in a multimodular integral pressurized water reactor is used to demonstrate the effectiveness and adaptability of the proposed data-recovery method. The computational results illustrate that the proposed method is robust to outliers and more capable than the existing FPCA-based method in terms of the accuracy in recovering strongly skewed signals. In addition, turbofan engine data are also analyzed to verify the capability of the proposed method in recovering non-skewed signals.

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI

PubMed Central

Xu, Tingting; Cullen, Kathryn R.; Mueller, Bryon; Schreiner, Mindy W.; Lim, Kelvin O.; Schulz, S. Charles; Parhi, Keshab K.

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03–0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03–0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works. PMID:26977400

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI.

PubMed

Xu, Tingting; Cullen, Kathryn R; Mueller, Bryon; Schreiner, Mindy W; Lim, Kelvin O; Schulz, S Charles; Parhi, Keshab K

2016-01-01

Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03-0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03-0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works.

Comparative analysis of numerical simulation techniques for incoherent imaging of extended objects through atmospheric turbulence

NASA Astrophysics Data System (ADS)

Lachinova, Svetlana L.; Vorontsov, Mikhail A.; Filimonov, Grigory A.; LeMaster, Daniel A.; Trippel, Matthew E.

2017-07-01

Computational efficiency and accuracy of wave-optics-based Monte-Carlo and brightness function numerical simulation techniques for incoherent imaging of extended objects through atmospheric turbulence are evaluated. Simulation results are compared with theoretical estimates based on known analytical solutions for the modulation transfer function of an imaging system and the long-exposure image of a Gaussian-shaped incoherent light source. It is shown that the accuracy of both techniques is comparable over the wide range of path lengths and atmospheric turbulence conditions, whereas the brightness function technique is advantageous in terms of the computational speed.

MONGKIE: an integrated tool for network analysis and visualization for multi-omics data.

PubMed

Jang, Yeongjun; Yu, Namhee; Seo, Jihae; Kim, Sun; Lee, Sanghyuk

2016-03-18

Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. .

Development and validation of a measure of pediatric oral health-related quality of life: the POQL

PubMed Central

Huntington, Noelle L; Spetter, Dante; Jones, Judith A.; Rich, Sharon E.; Garcia, Raul I.; Spiro, Avron

2011-01-01

Objective To develop a brief measure of oral health-related quality of life in children and demonstrate its reliability and validity in a diverse population. Methods We administered the initial 20-item POQL to children (Child Self-Report) and parents (Parent Report on Child) from diverse populations in both school-based and clinic-based settings. Clinical oral health status was measured on a subset of children. We used factor analysis to determine the underlying scales and then reduced the measure to 10 items based on several considerations. Multitrait analysis on the resulting 10-item POQL was used to reaffirm the discrimination of scales and assess the measure’s internal consistency and interscale correlations. We established discriminant and convergent validity with clinical status, perceived oral health and responses on the PedsQL and determined sensitivity to change with children undergoing ECC surgical repair. Results Factor analysis returned a four-scale solution for the initial items – Physical Functioning, Role Functioning, Social Functioning and Emotional Functioning. The reduced items represented the same four scales – two each on Physical and Role and three each on Social and Emotional. Good reliability and validity were shown for the POQL as a whole and for each of the scales. Conclusions The POQL is a valid and reliable measure of oral health-related quality of life for use in pre-school and school-aged children, with high utility for both clinical assessments and large-scale population studies. PMID:21972458

Development and validation of a measure of pediatric oral health-related quality of life: the POQL.

PubMed

Huntington, Noelle L; Spetter, Dante; Jones, Judith A; Rich, Sharron E; Garcia, Raul I; Spiro, Avron

2011-01-01

To develop a brief measure of oral health-related quality of life (OHQL) in children and demonstrate its reliability and validity in a diverse population. We administered the initial 20-item Pediatric Oral Health-Related Quality of Life (POQL) to children (Child Self-Report) and parents (Parent Report on Child) from diverse populations in both school-based and clinic-based settings. Clinical oral health status was measured on a subset of children. We used factor analysis to determine the underlying scales and then reduced the measure to 10 items based on several considerations. Multitrait analysis on the resulting 10-item POQL was used to reaffirm the discrimination of scales and assess the measure's internal consistency and interscale correlations. We established discriminant and convergent validity with clinical status, perceived oral health and responses on the PedsQL, and determined sensitivity to change with children undergoing ECC surgical repair. Factor analysis returned a four-scale solution for the initial items--Physical Functioning, Role Functioning, Social Functioning, and Emotional Functioning. The reduced items represented the same four scales--two each on Physical and Role and three each on Social and Emotional. Good reliability and validity were shown for the POQL as a whole and for each of the scales. The POQL is a valid and reliable measure of OHQL for use in preschool and school-aged children, with high utility for both clinical assessments and large-scale population studies.

Structural Control of Metabolic Flux

PubMed Central

Sajitz-Hermstein, Max; Nikoloski, Zoran

2013-01-01

Organisms have to continuously adapt to changing environmental conditions or undergo developmental transitions. To meet the accompanying change in metabolic demands, the molecular mechanisms of adaptation involve concerted interactions which ultimately induce a modification of the metabolic state, which is characterized by reaction fluxes and metabolite concentrations. These state transitions are the effect of simultaneously manipulating fluxes through several reactions. While metabolic control analysis has provided a powerful framework for elucidating the principles governing this orchestrated action to understand metabolic control, its applications are restricted by the limited availability of kinetic information. Here, we introduce structural metabolic control as a framework to examine individual reactions' potential to control metabolic functions, such as biomass production, based on structural modeling. The capability to carry out a metabolic function is determined using flux balance analysis (FBA). We examine structural metabolic control on the example of the central carbon metabolism of Escherichia coli by the recently introduced framework of functional centrality (FC). This framework is based on the Shapley value from cooperative game theory and FBA, and we demonstrate its superior ability to assign “share of control” to individual reactions with respect to metabolic functions and environmental conditions. A comparative analysis of various scenarios illustrates the usefulness of FC and its relations to other structural approaches pertaining to metabolic control. We propose a Monte Carlo algorithm to estimate FCs for large networks, based on the enumeration of elementary flux modes. We further give detailed biological interpretation of FCs for production of lactate and ATP under various respiratory conditions. PMID:24367246